{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Precision_Top_K_Metrics)=\n",
    "# Precision top-k metrics\n",
    "\n",
    "This section addresses the relevance of a credit card FDS from a more operational perspective, by explicitly considering their benefits for fraud investigators. Let us first recall that the purpose of an FDS is to provide investigators with *alerts*, that is, a set of transactions that are assumed to be the most suspicious (see Section {ref}`Fraud_Detection_System`). These transactions are manually checked, by contacting the cardholder. The process of contacting cardholders is time-consuming, and the number of fraud investigators is limited. The number of alerts that may be checked during a given period is therefore necessarily limited.\n",
    "\n",
    "*Precision top-$k$* metrics aim at quantifying the performance of an FDS in this setting {cite}`dal2017credit,dal2015adaptive,fan2011detection`. Precisions are computed daily, reflecting the precisions obtained for a working day of fraud investigators. The $k$ parameter quantifies the maximum number of alerts that can be checked by investigators in a day. \n",
    "\n",
    "Formally, for a day $d$, let us denote by $\\mathcal{T}_d$ the set of transactions. Only a minority of transactions $\\mathcal{T}_d^{Fraud} \\subset \\mathcal{T}_d$ is fraudulent, while the majority $\\mathcal{T}_d^{Genuine} \\subset \\mathcal{T}_d$ are genuine. Let us further denote by $\\mathcal{A}_d$ the set of alerts. The set of alerts contains both true positives ($\\mathcal{A}_d^{Fraud}$), and false positives ($\\mathcal{A}_d^{Genuine}$). Fig. 1 provides a schematic illustration.  \n",
    "\n",
    "![](images/hits_per_day_with_caption.png)\n",
    "\n",
    "Denoting by $k$ the number of alerts that investigators can process during a day, and by $|.|$ the cardinality of a set, we have $|\\mathcal{A}_d|=k$, with $k << |\\mathcal{T}_d|$. \n",
    "\n",
    "In this setting, the performance of a classifier is to maximize the precision in the subset $\\mathcal{A}_d$, that is, to maximize\n",
    "\n",
    "$$P@k(d)=\\frac{|\\mathcal{A}_d^{Fraud}|}{|\\mathcal{A}_d|}=\\frac{|\\mathcal{A}_d^{Fraud}|}{k}$$\n",
    "\n",
    "This quantity is referred to as the *Precision top-$k$ for day $d$* {cite}`dal2017credit,dal2015adaptive`, or $P@k(d)$. An alternative to this measure is the *Card Precision top-$k$*, which measures the *Precision top-$k$* in terms of cards rather than authorized transactions. Multiple transactions in $|A_d|$ from the same card should be counted as a single alert since investigators check all the recent transactions when contacting cardholders. The *Card Precision top-$k$ for day $d$* is referred to as $CP@k(d)$. \n",
    "\n",
    "We detail both these metrics in the following, together with their implementations. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Initialization: Load shared functions and simulated data \n",
    "\n",
    "# Load shared functions\n",
    "!curl -O https://raw.githubusercontent.com/Fraud-Detection-Handbook/fraud-detection-handbook/main/Chapter_References/shared_functions.py\n",
    "%run shared_functions.py\n",
    "\n",
    "# Get simulated data from Github repository\n",
    "if not os.path.exists(\"simulated-data-transformed\"):\n",
    "    !git clone https://github.com/Fraud-Detection-Handbook/simulated-data-transformed\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision top-$k$\n",
    "\n",
    "Let us reuse the [baseline fraud detection setup](Baseline_FDS), using one week of data for training, and one week of data for testing the detection performances. A decision tree of depth two is used as the prediction model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load  files\n",
      "CPU times: user 47.8 ms, sys: 49 ms, total: 96.8 ms\n",
      "Wall time: 140 ms\n",
      "201295 transactions loaded, containing 1792 fraudulent transactions\n"
     ]
    }
   ],
   "source": [
    "# Load data from the 2018-07-25 to the 2018-08-14\n",
    "\n",
    "DIR_INPUT='./simulated-data-transformed/data/' \n",
    "\n",
    "BEGIN_DATE = \"2018-07-25\"\n",
    "END_DATE = \"2018-08-14\"\n",
    "\n",
    "print(\"Load  files\")\n",
    "%time transactions_df=read_from_files(DIR_INPUT, BEGIN_DATE, END_DATE)\n",
    "print(\"{0} transactions loaded, containing {1} fraudulent transactions\".format(len(transactions_df),transactions_df.TX_FRAUD.sum()))\n",
    "\n",
    "start_date_training = datetime.datetime.strptime(\"2018-07-25\", \"%Y-%m-%d\")\n",
    "delta_train=delta_delay=delta_test=7\n",
    "\n",
    "(train_df,test_df)=get_train_test_set(transactions_df,start_date_training,\n",
    "                                      delta_train=delta_train,delta_delay=delta_delay,delta_test=delta_test)\n",
    "\n",
    "output_feature=\"TX_FRAUD\"\n",
    "\n",
    "input_features=['TX_AMOUNT','TX_DURING_WEEKEND', 'TX_DURING_NIGHT', 'CUSTOMER_ID_NB_TX_1DAY_WINDOW',\n",
    "       'CUSTOMER_ID_AVG_AMOUNT_1DAY_WINDOW', 'CUSTOMER_ID_NB_TX_7DAY_WINDOW',\n",
    "       'CUSTOMER_ID_AVG_AMOUNT_7DAY_WINDOW', 'CUSTOMER_ID_NB_TX_30DAY_WINDOW',\n",
    "       'CUSTOMER_ID_AVG_AMOUNT_30DAY_WINDOW', 'TERMINAL_ID_NB_TX_1DAY_WINDOW',\n",
    "       'TERMINAL_ID_RISK_1DAY_WINDOW', 'TERMINAL_ID_NB_TX_7DAY_WINDOW',\n",
    "       'TERMINAL_ID_RISK_7DAY_WINDOW', 'TERMINAL_ID_NB_TX_30DAY_WINDOW',\n",
    "       'TERMINAL_ID_RISK_30DAY_WINDOW']\n",
    "\n",
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth = 2, random_state=0)\n",
    "\n",
    "model_and_predictions_dictionary = fit_model_and_get_predictions(classifier, train_df, test_df, \n",
    "                                                                 input_features, output_feature)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictions for the test week are returned in the `predictions_test` entry of the `model_and_predictions_dictionary` dictionary. `predictions_test` is a vector that contains the fraud probabilities for each of the transactions in the `test_df` DataFrame. We can check that the number of predictions equals the number of transactions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(model_and_predictions_dictionary['predictions_test'])==len(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us add these predictions as a new column in the DataFrame of test transactions, and display the five first rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRANSACTION_ID</th>\n",
       "      <th>TX_DATETIME</th>\n",
       "      <th>CUSTOMER_ID</th>\n",
       "      <th>TERMINAL_ID</th>\n",
       "      <th>TX_AMOUNT</th>\n",
       "      <th>TX_TIME_DAYS</th>\n",
       "      <th>TX_FRAUD</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>134215</th>\n",
       "      <td>1236698</td>\n",
       "      <td>2018-08-08 00:01:14</td>\n",
       "      <td>2765</td>\n",
       "      <td>2747</td>\n",
       "      <td>-0.266899</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134216</th>\n",
       "      <td>1236699</td>\n",
       "      <td>2018-08-08 00:02:33</td>\n",
       "      <td>714</td>\n",
       "      <td>2073</td>\n",
       "      <td>1.295440</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134218</th>\n",
       "      <td>1236701</td>\n",
       "      <td>2018-08-08 00:08:40</td>\n",
       "      <td>4982</td>\n",
       "      <td>1258</td>\n",
       "      <td>-0.650902</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134219</th>\n",
       "      <td>1236702</td>\n",
       "      <td>2018-08-08 00:08:41</td>\n",
       "      <td>704</td>\n",
       "      <td>8501</td>\n",
       "      <td>0.290249</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134220</th>\n",
       "      <td>1236703</td>\n",
       "      <td>2018-08-08 00:10:34</td>\n",
       "      <td>3085</td>\n",
       "      <td>4208</td>\n",
       "      <td>0.039070</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TRANSACTION_ID         TX_DATETIME  CUSTOMER_ID  TERMINAL_ID  \\\n",
       "134215         1236698 2018-08-08 00:01:14         2765         2747   \n",
       "134216         1236699 2018-08-08 00:02:33          714         2073   \n",
       "134218         1236701 2018-08-08 00:08:40         4982         1258   \n",
       "134219         1236702 2018-08-08 00:08:41          704         8501   \n",
       "134220         1236703 2018-08-08 00:10:34         3085         4208   \n",
       "\n",
       "        TX_AMOUNT  TX_TIME_DAYS  TX_FRAUD  predictions  \n",
       "134215  -0.266899           129         0     0.003536  \n",
       "134216   1.295440           129         0     0.003536  \n",
       "134218  -0.650902           129         0     0.003536  \n",
       "134219   0.290249           129         0     0.003536  \n",
       "134220   0.039070           129         0     0.003536  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_df=test_df\n",
    "predictions_df['predictions']=model_and_predictions_dictionary['predictions_test']\n",
    "predictions_df[['TRANSACTION_ID','TX_DATETIME','CUSTOMER_ID','TERMINAL_ID','TX_AMOUNT','TX_TIME_DAYS','TX_FRAUD','predictions']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The $P@k(d)$ can be computed for a day $d$ by ranking all fraud probabilities by decreasing order, and computing the precision for the top $k$ ranked transactions. Let us for example compute the precision obtained on the top 100 transactions, for the first day of transactions in the test set (day 129)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def precision_top_k_day(df_day, top_k=100):\n",
    "    \n",
    "    # Order transactions by decreasing probabilities of frauds\n",
    "    df_day = df_day.sort_values(by=\"predictions\", ascending=False).reset_index(drop=False)\n",
    "        \n",
    "    # Get the top k most suspicious transactions\n",
    "    df_day_top_k=df_day.head(top_k)\n",
    "    list_detected_fraudulent_transactions=list(df_day_top_k[df_day_top_k.TX_FRAUD==1].TRANSACTION_ID)\n",
    "    \n",
    "    # Compute precision top k\n",
    "    precision_top_k = len(list_detected_fraudulent_transactions) / top_k\n",
    "    \n",
    "    return list_detected_fraudulent_transactions, precision_top_k\n",
    "\n",
    "\n",
    "day=129\n",
    "\n",
    "df_day = predictions_df[predictions_df['TX_TIME_DAYS']==day]\n",
    "df_day = df_day[['TRANSACTION_ID','CUSTOMER_ID', 'TX_FRAUD', 'predictions']]\n",
    "\n",
    "_, precision_top_k= precision_top_k_day(df_day=df_day, top_k=100)\n",
    "precision_top_k\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a precision of 0.26, that is, 26 out of the one hundred most highly suspicious transactions were indeed fraudulent transactions. \n",
    "\n",
    "It should be noted that the highest value that is achievable for the $P@k(d)$ may be lower than one. This is the case when $k$ is higher than the number of fraudulent transactions for a given day. In the example above, the number of fraudulent transactions at day 129 is 55."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day.TX_FRAUD.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a result, the maximum $P@100(129)$ which can be achieved is 55/100=0.55.\n",
    "\n",
    "When a test set spans multiple days, let $P@k$ be the mean of $P@k(d)$ for a set of days $d \\in \\mathcal{D}$ {cite}`dal2017credit,dal2015adaptive`, that is:\n",
    "\n",
    "$$\n",
    "P@k = \\frac{1}{|\\mathcal{D}|}\\sum_{d \\in \\mathcal{D}} P_k(d)\n",
    "$$\n",
    "\n",
    "Let us implement this score with a function `precision_top_k`, which takes as input a `predictions_df` DataFrame, and a `top_k` threshold. The function loops over all days of the DataFrame, and computes for each day the number of fraudulent transactions and the precision top-$k$. It returns these values for all days as lists, together with the resulting mean of the precisions top-$k$.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def precision_top_k(predictions_df, top_k=100):\n",
    "\n",
    "    # Sort days by increasing order\n",
    "    list_days=list(predictions_df['TX_TIME_DAYS'].unique())\n",
    "    list_days.sort()\n",
    "    \n",
    "    precision_top_k_per_day_list = []\n",
    "    nb_fraudulent_transactions_per_day = []\n",
    "    \n",
    "    # For each day, compute precision top k\n",
    "    for day in list_days:\n",
    "        \n",
    "        df_day = predictions_df[predictions_df['TX_TIME_DAYS']==day]\n",
    "        df_day = df_day[['TRANSACTION_ID', 'CUSTOMER_ID', 'TX_FRAUD', 'predictions']]\n",
    "        \n",
    "        nb_fraudulent_transactions_per_day.append(len(df_day[df_day.TX_FRAUD==1]))\n",
    "        \n",
    "        _, _precision_top_k = precision_top_k_day(df_day, top_k=top_k)\n",
    "        \n",
    "        precision_top_k_per_day_list.append(_precision_top_k)\n",
    "        \n",
    "    # Compute the mean\n",
    "    mean_precision_top_k = np.round(np.array(precision_top_k_per_day_list).mean(),3)\n",
    "    \n",
    "    # Returns number of fraudulent transactions per day,\n",
    "    # precision top k per day, and resulting mean\n",
    "    return nb_fraudulent_transactions_per_day,precision_top_k_per_day_list,mean_precision_top_k\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying this function to the `prediction_df`, with $k=100$, we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of remaining fraudulent transactions: [55, 60, 56, 56, 59, 58, 41]\n",
      "Precision top-k: [0.26, 0.35, 0.26, 0.33, 0.3, 0.35, 0.19]\n",
      "Average Precision top-k: 0.291\n"
     ]
    }
   ],
   "source": [
    "nb_fraudulent_transactions_per_day_remaining,\\\n",
    "precision_top_k_per_day_list,\\\n",
    "mean_precision_top_k = precision_top_k(predictions_df=predictions_df, top_k=100)\n",
    "\n",
    "print(\"Number of remaining fraudulent transactions: \"+str(nb_fraudulent_transactions_per_day_remaining))\n",
    "print(\"Precision top-k: \"+str(precision_top_k_per_day_list))\n",
    "print(\"Average Precision top-k: \"+str(mean_precision_top_k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For better visualization, let us plot these data. We reuse the plotting function introduced in {ref}`Baseline_FDS_Training_Test_Sets` (imported from the [shared functions](shared_functions) notebook). First, we use the `get_tx_stats` function to compute the number of transactions per day, fraudulent transactions per day, and compromised cards per day. We then add the *remaining* fraudulent transactions per day, and precision top-$k$ per day to the DataFrame, for the seven days of the test period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the number of transactions per day, \n",
    "#fraudulent transactions per day and fraudulent cards per day\n",
    "tx_stats=get_tx_stats(transactions_df, start_date_df=\"2018-04-01\")\n",
    "\n",
    "# Add the remaining number of fraudulent transactions for the last 7 days (test period)\n",
    "tx_stats.loc[14:20,'nb_fraudulent_transactions_per_day_remaining']=list(nb_fraudulent_transactions_per_day_remaining)\n",
    "# Add precision top k for the last 7 days (test period) \n",
    "tx_stats.loc[14:20,'precision_top_k_per_day']=precision_top_k_per_day_list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the *remaining* number of fraudulent transactions is the number of fraudulent transactions originally present in the test period, minus those transactions that belong to cards known to be compromised in the training set (see `get_train_test_set` function). We recall that these transactions should be removed when assessing the performance of the fraud detector on the test set, since the cards they belong to were known to be compromised when training the fraud detector. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "# Plot the number of transactions per day, fraudulent transactions per day and fraudulent cards per day\n",
    "\n",
    "cmap = plt.get_cmap('jet')\n",
    "colors={'precision_top_k_per_day':cmap(0), \n",
    "        'nb_fraudulent_transactions_per_day':cmap(200),\n",
    "        'nb_fraudulent_transactions_per_day_remaining':cmap(250),\n",
    "       }\n",
    "\n",
    "fraud_and_transactions_stats_fig, ax = plt.subplots(1, 1, figsize=(15,8))\n",
    "\n",
    "# Training period\n",
    "start_date_training = datetime.datetime.strptime(\"2018-07-25\", \"%Y-%m-%d\")\n",
    "delta_train = delta_delay = delta_test = 7\n",
    "\n",
    "end_date_training = start_date_training+datetime.timedelta(days=delta_train-1)\n",
    "\n",
    "# Test period\n",
    "start_date_test = start_date_training+datetime.timedelta(days=delta_train+delta_delay)\n",
    "end_date_test = start_date_training+datetime.timedelta(days=delta_train+delta_delay+delta_test-1)\n",
    "\n",
    "get_template_tx_stats(ax, fs=20,\n",
    "                      start_date_training=start_date_training,\n",
    "                      title='Number of fraudulent transactions per day \\n and number of detected fraudulent transactions',\n",
    "                      delta_train=delta_train,\n",
    "                      delta_delay=delta_delay,\n",
    "                      delta_test=delta_test,\n",
    "                      ylim=150\n",
    "                     )\n",
    "\n",
    "ax.plot(tx_stats['tx_date'], tx_stats['nb_fraudulent_transactions_per_day'], 'b', color=colors['nb_fraudulent_transactions_per_day'], label = '# fraudulent txs per day - Original')\n",
    "ax.plot(tx_stats['tx_date'], tx_stats['nb_fraudulent_transactions_per_day_remaining'], 'b', color=colors['nb_fraudulent_transactions_per_day_remaining'], label = '# fraudulent txs per day - Remaining')\n",
    "ax.plot(tx_stats['tx_date'], tx_stats['precision_top_k_per_day']*100, 'b', color=colors['precision_top_k_per_day'], label = '# detected fraudulent txs per day')\n",
    "ax.legend(loc = 'upper left',bbox_to_anchor=(1.05, 1),fontsize=20)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting plot gives us \n",
    "\n",
    "* the number of fraudulent transactions originally present in the transaction dataset, for the training, delay, and test period\n",
    "* the number of remaining transactions for the test period (original, minus those belonging to cards known to be fraudulent in the training period)\n",
    "* the number of detected fraudulent transactions for the test period (precision top-$k$ times $k$). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_and_transactions_stats_fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph shows a well-detailed summary of the performance of the fraud detector from an operational point of view. In the test period, the number of fraudulent transactions varied between 74 and 91. Once known compromised cards from the training period were removed, the remaining fraudulent transactions varied between 41 and 60. Out of these remaining fraudulent transactions, the fraud detector correctly detected between 19 and 35 transactions, with an average number of 29 fraudulent transactions detected per day (average precision top-$k$=0.29), that is, about 50% of the actual fraudulent transactions were detected.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Card_Precision_Top_K)=\n",
    "## Card Precision top-$k$\n",
    "\n",
    "As pointed out in the introduction, multiple fraudulent transactions from the same card should count as a single correct detection since investigators check all the recent transactions when contacting cardholders. The resulting metric is the *Card Precision Top-$k$*, or $CP@k$, and quantifies the number of correctly detected compromised cards out of the $k$ cards which have the highest risks of frauds.\n",
    "\n",
    "$CP@k$ can be computed by slightly amending the implementation of the Precision top-$k$ given above. More specifically, instead of simply sorting transactions by decreasing order of their fraud probabilities, we first group transactions by customer ID. For each customer ID, we then take the maximum value of the fraud probability and the fraud label. This can be computed in a single line of code with the Panda `group_by` and `max` operators. The card precision top-$k$ is finally computed by sorting customer IDs by decreasing order the fraud probabilities and computing the precision for the set of $k$ cards with the highest fraud probabilities. \n",
    "\n",
    "This is implemented below with the function `card_precision_top_k_day`. Similarly to the `precision_top_k_day`, the function takes as input a set of transactions for a given day and a top-$k$ value. It returns the list of detected compromised cards, and the card precision top-$k$ for that day. As an example, let us compute the precision obtained on the top 100 cards, for the first day of transactions in the test set (day 129)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def card_precision_top_k_day(df_day, top_k):\n",
    "    \n",
    "    # This takes the max of the predictions AND the max of label TX_FRAUD for each CUSTOMER_ID, \n",
    "    # and sorts by decreasing order of fraudulent prediction\n",
    "    df_day = df_day.groupby('CUSTOMER_ID').max().sort_values(by=\"predictions\", ascending=False).reset_index(drop=False)\n",
    "    \n",
    "    # Get the top k most suspicious cards\n",
    "    df_day_top_k=df_day.head(top_k)\n",
    "    list_detected_compromised_cards=list(df_day_top_k[df_day_top_k.TX_FRAUD==1].CUSTOMER_ID)\n",
    "    \n",
    "    # Compute precision top k\n",
    "    card_precision_top_k = len(list_detected_compromised_cards) / top_k\n",
    "    \n",
    "    return list_detected_compromised_cards, card_precision_top_k\n",
    "\n",
    "day=129\n",
    "\n",
    "df_day = predictions_df[predictions_df['TX_TIME_DAYS']==day]\n",
    "df_day = df_day[['predictions', 'CUSTOMER_ID', 'TX_FRAUD']]\n",
    "\n",
    "_,card_precision_top_k= card_precision_top_k_day(df_day=df_day, top_k=100)\n",
    "card_precision_top_k\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a card precision of 0.24, that is, 24 out of the first one hundred most highly suspicious cards were indeed compromised cards. The number of compromised cards on day 129 was 50. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day.groupby('CUSTOMER_ID').max().TX_FRAUD.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close to 50% of the compromised cards were therefore detected. Computing the Card Precision top-$k$ over several days is achieved the same way as for the Precision Top-$k$, by computing the Card Precision top-$k$ for each day of the test period, and taking the mean. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def card_precision_top_k(predictions_df, top_k):\n",
    "\n",
    "    # Sort days by increasing order\n",
    "    list_days=list(predictions_df['TX_TIME_DAYS'].unique())\n",
    "    list_days.sort()\n",
    "    \n",
    "    card_precision_top_k_per_day_list = []\n",
    "    nb_compromised_cards_per_day = []\n",
    "    \n",
    "    # For each day, compute precision top k\n",
    "    for day in list_days:\n",
    "        \n",
    "        df_day = predictions_df[predictions_df['TX_TIME_DAYS']==day]\n",
    "        df_day = df_day[['predictions', 'CUSTOMER_ID', 'TX_FRAUD']]\n",
    "        \n",
    "        nb_compromised_cards_per_day.append(len(df_day[df_day.TX_FRAUD==1].CUSTOMER_ID.unique()))\n",
    "        \n",
    "        _, card_precision_top_k = card_precision_top_k_day(df_day,top_k)\n",
    "        \n",
    "        card_precision_top_k_per_day_list.append(card_precision_top_k)\n",
    "        \n",
    "    # Compute the mean\n",
    "    mean_card_precision_top_k = np.array(card_precision_top_k_per_day_list).mean()\n",
    "    \n",
    "    # Returns precision top k per day as a list, and resulting mean\n",
    "    return nb_compromised_cards_per_day,card_precision_top_k_per_day_list,mean_card_precision_top_k\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of remaining compromised cards: [50, 54, 51, 54, 55, 54, 38]\n",
      "Precision top-k: [0.24, 0.35, 0.26, 0.32, 0.3, 0.34, 0.19]\n",
      "Average Precision top-k: 0.2857142857142857\n"
     ]
    }
   ],
   "source": [
    "nb_compromised_cards_per_day_remaining\\\n",
    ",card_precision_top_k_per_day_list\\\n",
    ",mean_card_precision_top_k=card_precision_top_k(predictions_df=predictions_df, top_k=100)\n",
    "\n",
    "print(\"Number of remaining compromised cards: \"+str(nb_compromised_cards_per_day_remaining))\n",
    "print(\"Precision top-k: \"+str(card_precision_top_k_per_day_list))\n",
    "print(\"Average Precision top-k: \"+str(mean_card_precision_top_k))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us plot these results for better visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the number of transactions per day, \n",
    "# fraudulent transactions per day and fraudulent cards per day\n",
    "tx_stats=get_tx_stats(transactions_df, start_date_df=\"2018-04-01\")\n",
    "\n",
    "# Add the remaining number of compromised cards for the last 7 days (test period)\n",
    "tx_stats.loc[14:20,'nb_compromised_cards_per_day_remaining']=list(nb_compromised_cards_per_day_remaining)\n",
    "\n",
    "# Add the card precision top k for the last 7 days (test period) \n",
    "tx_stats.loc[14:20,'card_precision_top_k_per_day']=card_precision_top_k_per_day_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "# Plot the number of transactions per day, fraudulent transactions per day and fraudulent cards per day\n",
    "\n",
    "cmap = plt.get_cmap('jet')\n",
    "colors={'card_precision_top_k_per_day':cmap(0), \n",
    "        'nb_compromised_cards_per_day':cmap(200),\n",
    "        'nb_compromised_cards_per_day_remaining':cmap(250),\n",
    "       }\n",
    "\n",
    "fraud_and_transactions_stats_fig, ax = plt.subplots(1, 1, figsize=(15,8))\n",
    "\n",
    "# Training period\n",
    "start_date_training = datetime.datetime.strptime(\"2018-07-25\", \"%Y-%m-%d\")\n",
    "delta_train = delta_delay = delta_test = 7\n",
    "\n",
    "end_date_training = start_date_training+datetime.timedelta(days=delta_train-1)\n",
    "\n",
    "# Test period\n",
    "start_date_test = start_date_training+datetime.timedelta(days=delta_train+delta_delay)\n",
    "end_date_test = start_date_training+datetime.timedelta(days=delta_train+delta_delay+delta_test-1)\n",
    "\n",
    "get_template_tx_stats(ax, fs=20,\n",
    "                      start_date_training=start_date_training,\n",
    "                      title='Number of fraudulent transactions per day \\n and number of detected fraudulent transactions',\n",
    "                      delta_train=delta_train,\n",
    "                      delta_delay=delta_delay,\n",
    "                      delta_test=delta_test,\n",
    "                      ylim=150\n",
    "                     )\n",
    "\n",
    "ax.plot(tx_stats['tx_date'], tx_stats['nb_compromised_cards_per_day'], 'b', color=colors['nb_compromised_cards_per_day'], label = '# fraudulent txs per day - Original')\n",
    "ax.plot(tx_stats['tx_date'], tx_stats['nb_compromised_cards_per_day_remaining'], 'b', color=colors['nb_compromised_cards_per_day_remaining'], label = '# compromised cards per day - Remaining')\n",
    "ax.plot(tx_stats['tx_date'], tx_stats['card_precision_top_k_per_day']*100, 'b', color=colors['card_precision_top_k_per_day'], label = '# detected compromised cards per day')\n",
    "ax.legend(loc = 'upper left', bbox_to_anchor=(1.05, 1), fontsize=20)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABX0AAAIpCAYAAAD+V5uvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeVxN+f8H8JdUIlHIEhpqlKWIRihEtiFbtiSU7DPZJutg7NvYZjLWmdJCyZIQyVaEkqUkUmmhkrpp13JvdX5/9Ljnd2936VY3mfm+n39d55zPOZ9zzud+rt7nfT6fRgzDMCCEEEIIIYQQQgghhBDyn6DQ0BUghBBCCCGEEEIIIYQQIj8U9CWEEEIIIYQQQgghhJD/EAr6EkIIIYQQQgghhBBCyH8IBX0JIYQQQgghhBBCCCHkP4SCvoQQQgghhBBCCCGEEPIfQkFfQgghhBBCCCGEEEII+Q+hoC8hAubMmQN9fX3o6+vj+PHjMpfbvn07Wy41NbUea1h/+OduaGjY0FX5pvB4PBw/fhzjxo2DkZERjI2N8eOPPyIiIkLmfXA4HPz2228YPnw4DA0NMXDgQEycOBGlpaX1WPOGc+TIkXr7Pjx58oTd96lTp+S6b3lLTU1FYWFhnfbx9u1bOdWG1AXDMIiLixO7jt8e58+f/5VrRRpKffZxhBBCCCGEyAsFfQmR4NixY0hISGjoapAGtmbNGvzxxx9ISEhAcXExCgsLkZSUhLZt28pUvqioCDY2NvDx8cHHjx/B5XKRk5ODwsJCNGnSpJ5rTxoCl8vF0aNHYWlpidzc3Frtg8PhYPXq1fjpp5/kXDtSU9HR0bC2toarq2tDV4UQQgghhBBCZKbY0BUg5FvF5XKxceNGeHl5QUGBno/8L0pISEBAQAAAoGPHjnByckKnTp2Ql5eHjh07yrSPK1euICUlBQBgYmKChQsXokWLFmAYpt7qTRrW33//DWdn5zrtY/Xq1QgLC5O5nZH6M336dFRUVEBHR6ehq0IIIYQQQgghMqOgLyFSREREwMPDA/b29g1dFdIA3r17x35esmQJLC0t67SPHTt2oEuXLvKoGvmGVVRUfBP7IPJR3b2IjY39SjUhhBBCCCGEENlR+iIhYigoKEBRsfKZyB9//MFmapL/LcXFxeznTp061WofRUVFdd4HIYQQQgghhBBCSE1Q0JcQMRQVFdlJeYqLi7Fp06YGrhFpCIIZfo0bN67VPgSHceA/SCCEEEIIIYQQQgipTxSBIEQCR0dH3L59G4mJiQgLC8P58+cxY8aMGu/nyJEj+OuvvwAAPj4+MDIyErvd9u3bcfbsWQDA3bt3hbJC58yZg/DwcIwZMwbOzs54/vw53N3d8eLFC+Tn50NTUxPDhw/HokWL2AnGPnz4ABcXF4SEhCAzMxNqamowNjbG4sWLYWhoWG29nz59ChcXF0RERKCkpATt27eHubk57O3toaWlJbVscXExvL29cefOHSQmJqKwsBDq6uowMDDAhAkTMHbsWLHjJD958gRz584FAPj5+SEzMxOHDx9GQkICVFVVoa+vj99//x3t2rWrtv58XC4Xfn5+CAwMRExMDPLz86Gmpobvv/8eI0eOhLW1NVRUVITK8K+3IH69AMDDwwMDBgyQelx9fX2py/ivhFtYWCAtLQ1z587F4sWLsWPHDoSEhIBhGHTq1AmLFi3ChAkT2HJZWVk4f/48QkNDkZSUhLy8PCgqKkJdXR2GhoYYN24cxowZg0aNGgkdOzU1FSNGjAAAWFtbY/v27WLr/erVK0ybNg1A5Xdg2bJlItuUlZUhICAAFy5cYO9v586dMWHChGqHQuGfb9euXXHz5k2J2/Xt2xdFRUUwMTGBp6en1H2Kk5CQgDNnziA0NBQZGRlgGAbt27fHgAEDMGfOHHz//fdiy61fvx6XL19G9+7dceXKFSQnJ8Pd3R0PHz5ERkYGmjZtCj09PUyePBlWVlZC7djX1xcbNmwQ2h//mnfs2BH37t2rtt784/OlpaWx7cbKygp79+4F8P9tdMSIEdi/fz/27t2LW7dusd9VGxsboXtRWFiIixcv4uHDh4iLi0NeXh4YhkHLli3Rs2dPjBo1CpMmTYKSkpJInQTb6MaNGxEaGgpvb29EREQgJycH6urq6NevH2bNmoWBAwdKPLdXr17Bx8cHT58+RXp6OhQUFNCqVSsYGRlh3LhxGDFihEi7FfTs2TNcu3YNL168QGZmJgoLC6Gqqop27drBxMQEs2bNgq6urtTrGxUVhQsXLuDFixf4+PEjAKBz584YMmQI7OzshCZo5J833+XLl9l7s2fPHkyZMgXA/3+vBw8eDBcXF7HHjY+Px9mzZ/HkyRN8+vQJDMOgbdu26N+/P2xsbGBgYCC2HP/3Q01NDc+ePQOHw8Hp06cRFBSE9PR0KCoqQldXF2PHjsWsWbOgrKwsdj95eXnw9vZGcHAw3r17h5KSErRo0QI6OjoYOnQoZs6ciRYtWki9duIItvmHDx+iUaNGOH78OIKCgpCZmYnmzZujT58+sLGxwbBhw6rdX0hICHx9fREZGYmsrCyoqKhAW1sb5ubmmD17Nlq1aiW2XE370erUpY/jq01fffPmTaxYsQIA8PPPP2P58uUS95+ZmYlhw4ahvLwcDg4OWLduncznRwghhBBC/jdQ0JcQCZSVlbFr1y7Y2tqioqICv//+O8zNzWsUcKwPJ06cwB9//CGUQZqamgpPT0/cuXMHPj4+ePPmDZycnPDlyxd2m+zsbNy+fRvBwcE4fvw4hgwZIvEYbm5u2Lt3r9AxkpOTkZycDB8fH+zduxdjx44VWzYqKgqOjo7IyMgQWs7hcBAUFISgoCB4enrC2dlZKMBS1f379/Hnn3+y2bZcLhdpaWlSy1T19u1brFy5EklJSULLs7OzER4ejvDwcJw+fRpHjhyRKRBenwoLCzF79myhusbFxQm1t8uXL2Pr1q0oKSkRKsvlclFUVISPHz8iMDAQw4cPx9GjR2udnSxNbm4ulixZgoiICKHlcXFxOHjwIPz9/dG/f3+5H7cmjh07hr/++gvl5eVCy5OSkpCUlITz58/jp59+gqOjo9QgY2BgINatWyc0zEdpaSnbdvz9/XHy5EmJgbavoaysDIsWLcKzZ8/YZcnJyWjZsiX775CQEDg5OSEvL0+kfGZmJjIzMxEcHIzz58/D1dUVzZs3l3i8/fv3459//hFaxuFwEBgYiMDAQCxduhQrV64UKff333/j4MGDIhMYpqWlIS0tDdevX4epqSmOHj2KZs2aCW1TUlKCtWvXIjAwUGS/eXl5yMvLQ1xcHM6dO4c9e/Zg4sSJItvxeDzs3bsXZ86cEVkXGxuL2NhY+Pj4wNnZGaamphLPv6YYhsHBgwfh4uIiMjbw+/fv8f79e1y6dAm2trbYsGGD1LcBnj17hp9//hm5ublCyyMjIxEZGQlfX194eHhAXV1daP3bt2+xYMECcDgcoeWfP3/G58+f2Qd8J06cQN++fWt9rklJSfjll1+EjpOTk4Pg4GAEBwdj1qxZ2Lx5s9gHfkVFRVi7di1u374ttJzL5SI6OhrR0dFwd3fH/v37YWFhIbEOsvSj1ZFHH1fbvtrCwgLq6urIzc3FtWvXpAZ9/f392T7OyspK5vMjhBBCCCH/OyjoS4gU/fr1g62tLTw9PVFQUIAtW7bgxIkTDVaf8PBwBAYGol27dpg/fz4MDAzA4XBw4sQJxMTEID09HatXr0ZUVBSaNGmCVatWoX///uByubh48SL8/f3B4/Gwbds23Lp1S+wf31wuF3v27IGKigrs7e0xZMgQlJWV4c6dO/D29kZJSQmcnJygpaWFPn36CJWNj4+HnZ0dioqK0LRpU9jY2MDMzAwtWrRAeno6rl+/jsDAQERERMDBwQE+Pj5QVVUVe65//vkn1NTUsGLFCvTo0QNxcXFQVlaWGqQTlJycjHnz5iE7OxsAMHz4cEyePBlaWlrIzMyEv78/AgICkJ6ejrlz58LHxwd6enoAgJ07d6KoqAh3797FkSNH2GX8bDxtbe1qj+/n58eeR1BQkNAySdtXVFRg2rRpmDx5MgoKCvD48WOYmJgAAEJDQ7F+/XoAgLq6OmxtbWFkZISWLVsiIyODzUbn8XgICgrChQsXMHPmTJmulawqKiqwcOFCREVFAQD69+8PW1tbaGlp4f379/Dw8MCrV68QHx8v1+PWhGBmva6uLmxtbdGjRw8AQExMDDw8PJCcnMxuIy6TGQD7XWrcuDEWLFiAwYMHQ1lZGc+fP8fJkydRWFiIx48fw83NDYsWLQJQGbDx8/ODt7c3fHx8AACnTp1C27ZtxWbQirN8+XLY2dlh48aNeP36NTQ1NfH3338DgFAgly8kJAQVFRWwsLDA3LlzUV5ejjt37uDHH38EACQmJmLp0qXg8XhQUVGBtbU1TE1NoaGhgaysLLx8+RKenp4oKirCy5cvcfLkSTg5OYmtW0BAADgcDjp06AB7e3v06dMHJSUluHXrFry9vcEwDI4fP45hw4YJvdHw9OlTNuDbvXt32NnZoWvXrqioqEBiYiLc3Nzw7t07PH78GM7Ozmw759uyZQsb8DUwMICNjQ20tbWhoKCAlJQU+Pj4ICIiAmVlZdiyZQuGDBkCDQ0NoX1s3ryZzdLt1KkT7O3t0atXL3z58gVBQUHw8fFBQUEBfv75Z1y9ehWdO3fGqVOnwOPxMHnyZACVfQg/C7NDhw4y3c/du3fDw8MDAKChoQF7e3sYGxtDQUEBUVFRcHV1RWZmJs6cOYMvX76wmdxVlZSU4KeffsKXL19gY2ODESNGoHnz5njz5g1OnDiBzMxMxMbG4vDhw9i2bRtbrry8HCtWrACHw0GzZs0wf/58GBsbQ1VVFRwOBwEBAbh27Rpyc3OxYsUK3Lp1S+TNB1nxA74DBgzA7Nmz0bZtW7Z+GRkZ8PLyQrNmzbBmzRqhchUVFVi6dCnCwsIAVGZMW1lZQVtbG1++fEF4eDjOnDmD/Px8ODo6wsXFBYMGDRJbh+r60erIo4+rS1+trKyM8ePH48yZM/jw4QMiIyMlvh105coVAECvXr3Y3y5CCCGEEEIEUdCXkGr88ssvuHfvHtLS0hAUFIRr167V6DVRecrJyUHbtm1x4cIFocylAQMGYNiwYSgpKUF4eDhatGgBHx8fdO3ald1m0KBB4PF4CAwMREpKCuLi4tC9e3exx2nWrBnc3d3Ru3dvdtnAgQNhamqKn376CeXl5di+fTsuXbokVG7NmjUoKipC69at4enpKfSqde/evTFmzBicP38emzdvRnx8PI4fP47Vq1eLrUNFRQUOHz4MMzMzAJUB+JrYtm0bG/Bdv3495s2bJ7R+5MiRMDc3x/r161FUVITVq1fjypUraNSoEb777jsAlUFCPm1tbTZ4KAv+toJZd9LKV1RUYPz48di1axe7TDCjzdnZGUDluMD//POPSGby6NGjMWTIECxZsgRA5WvC8g76Xrx4kQ2GTJo0Cfv27WOD8H369MG4ceOwcuVKkWy9r+X169c4duwYAGDMmDE4cOCAUBZuv379MHXqVCxZsgShoaE4evQoxo4dK3aoh7y8PDRr1gxeXl5C983Y2BgmJiaYOXMmGIbB5cuX2aCvuro61NXVoampyW6vq6tbown8tLS0oKWlxT4MUVZWrrbdGBsb4+jRo+xDnMGDB7Prjx07Bh6PBwA4ePAgRo4cKVR+xIgRGDt2LKZNm4aysjLcvHlTYtCXw+GgW7duOHv2rFAAetCgQdDS0sKBAwcAVGY5CgaqfH19wTAM1NXVcebMGaipqbHrjI2NMXbsWFhZWeHDhw+4ePEi1qxZw2Y+Jicns8GtPn364MyZM0L39IcffoCVlRWWL1+OwMBAFBUV4cGDB5g0aRK7zePHj9mAb9++ffHPP/8IZTMPGTIEhoaGbF9w/Phx7N69W6RdqKur16gPeP78ORvw7dq1Kzw8PITeVDA2NoaVlRUcHBzw+vVrXL58GRYWFhg9erTIvng8HgoLC3HixAkMHTqUXd63b18MHToUEyZMQHFxMfz9/bFp0yb2IcPz58+RnJwMoLJPrJoFPWLECLRt2xYuLi7IyMjA/fv3MWbMGJnPURCHw8GMGTOwfft2tl8wMjLC6NGjMWvWLLx//x7u7u6YNm2a0G+Th4cHG/Bdt24dHBwchPY7aNAgTJ06FTY2NsjMzMSGDRtw+/ZtsQ9SqutHqyOPPq6ufbWVlRWbkX7t2jWxQd/Y2Fi8ffsWANiHEoQQQgghhFRFE7kRUo1mzZphx44d7L937drFBhMbwqJFi0ReVdXQ0BDKZJo7d67QH9V8gn/8vn//XuIxHB0dhQK+guX5r5FGR0fj1atX7LpHjx6xQdJffvlF4tiaM2bMYF+f9vb2ZgNSVWlra7MB35qKiYnB48ePAQDDhg0TCfjyWVlZseNyxsbG4sGDB7U6nrzY2NiIXV5cXIzS0lK0bNkSw4cPlzgUxfDhw9lxOasOryEP586dA1CZcbplyxaRrGtFRUXs3r1bKKj3Nbm6uqKiogLNmjXDrl27xA67oKKigt27d0NBQQEMw0gdL9jGxkZskM/IyIgdxzUxMRFcLld+J1EL1tbWYrP2gcqhTFq1aoWePXuKBHz5evTowQY4q2s3a9asEZtxPGPGDLY98Mer5uO/7t+6dWuxbaN58+ZYuXIl5s2bh1WrVqG0tJRdFxcXh++++w7KyspYvHixxKE0BIOZVc+Bn3WtoKCAffv2iR2+wsrKCj179gRQGSSuOgxFbQiO7/v777+LHZpGXV0df/zxBxvk5md1izNq1CihgC9f586d2T61sLAQqamp7LqsrCz2c5cuXcTu187ODjNmzICTkxP7wKs2unTpgs2bN4v0C23atGGzj3k8Hi5evMiuq6iogJubG4DKAH7VgC9fp06d2Azh9PR0qUFXSf2oLOrax8mjrzYwMGAzd2/cuIGysjKR8vwHIUpKShg/fnwNzpAQQgghhPwvoaAvITIwMzNjg4M5OTlCQeCvTdJ4k4KBYEmvvrZu3Zr9XFRUJHYbBQUFTJ8+XeLxBbOKHj16xH4ODg5mP1cXrDU3NwdQGaAQDBwLqjp0RE2EhISwn62traVuKxggECz3tSkqKkoMEDRt2hS+vr4IDw9ns8gkadOmDQDIPRCZlZWFN2/eAKgMWEgalqNFixYYNWqUXI8tC4Zh2PvXu3dvqYFnLS0tdOvWDQDYDENxBDNmqxIc4kNw7OyGIOn1b6AyEB4aGiqSlV+VLO1GUVFR4kRtLVu2ZIPBVa+Hjo4OgMrJ9TZt2iQ0ORqfpaUl1q9fD1tbW6ExfUePHo3AwEBERUVJzdjk17/qOZSVlbHtom/fvlKDms7Ozrh//z6CgoJkHkZGkrKyMrZt9erVS+xDND5tbW22rb169Qo5OTlit6tNe+RfewDYsGEDQkNDRcYWbteuHXbs2IFFixZJfPtDFtOmTZMYlOdngwNgh7sBKh8QpKenA5D828Y3dOhQ9r6EhoaK3UZaP1odefRx8uqr+f/fyM7OFvqdBSoD5f7+/gAqf0slTW5HCCGEEEIIDe9AiIw2bNiAkJAQcDgc3LhxA5aWlhIz5+pTx44dxS4X/GNb8PVySdtIymTr0qWL1Fnc+dlwQGUWHp/gUAiyzNLOl5KSInboBlnHzBRHcLxFaQExoPJ8lJSUwOPxhM7na9PQ0ECTJk2q3Y6f0VlUVITU1FR8+PABiYmJiI2NxfPnz9kAijwyFQUlJSWx+6zuFXdDQ0P4+vrK9fjVSU1NZScqCwsLYzNxZSknibRhGQQDk1UnjPvaZPmu8NtNaWkpUlNTkZqaiqSkJMTGxuLFixfsEADS2k2bNm2kttFmzZohNzdXJDPR1tYWFy9exJcvX3DhwgVcuHAB3bp1g6mpKUxNTWFiYiIyeVtVgkHYrKwspKSk4P3790hISEB0dLTQpFuC58DhcNggqGDfJU7nzp2lrq+Jjx8/sseV5QFWnz59cP/+fTAMg3fv3omdKKw27bF79+4YOnQoHjx4gHfv3sHe3h7q6uoYNGgQTE1NYWZmJvE3paaqG4KnZ8+e+PjxI5KTk1FRUQEFBQU2yApUBt2rC5TypaSkiF0uaz8qjrz7uLr01RMnTsTBgwfB4/Fw9epV9kEpUNm/8bODaQI3QgghhBAiDQV9CZFRixYtsHXrVvz8888AgK1bt8LExERqgFTeGjduLNMkO/xXhWtDMBtYHDU1NTZIyg+yAZCYnVad/Px8scvFvYItK/7s9goKCiITOlWlqKgIdXV1cDgctlxDkJRVJujTp09wdXXFvXv3JAY9FBQURDL55EHwNXHBcYrFEcy6/Fpq2/7KyspQWFgotr01bdpUYjnBIKS8A+w1oaSkJDG7ki83Nxfu7u4IDAxEUlKS2PYhS7uRdj2A/78mVa/Hd999BxcXF/z6669ITEwEUPlgJj4+Hu7u7lBWVoaZmRlmzpwp8YFRSEgIvLy88OzZM7F9hqThLWrSbuVJsC+prk8FhL8zkvqh2rZH/uRu165dA8MwyM3NRUBAAAICAgAAenp6mDBhAmxtbWXqhyQRN3yFIH5Ganl5OXJyctC6dWu5/27Upf7y7OPq2le3bt0aQ4YMwb1793D37l0UFRWxgX3+0A7q6upCwWBCCCGEEEKqoqAvITUwcuRI/Pjjj7h58yY4HA727t2L3bt3y2XfsgTq6hLMlSd+UEFwIh1+dp+GhgZOnz4t876qjk/MV5fXq2sa9ORvLylw9DVUd74PHjzAihUrhIblUFVVha6uLr7//nsYGhrC1NQUjo6OUmeWr46kAGZN7oeiYt1/Wmp6DwWzG6dOnYo5c+bIXLa6YOa3rLr7Eh0djUWLFuHz58/sMhUVFejo6EBXVxcGBgYYOHAgDh8+LDREi7z17dsX169fx5MnT3D79m2EhITgw4cPACpfbw8KCkJQUBAmTpyIffv2sd9FhmGwefNmXLhwQWh/HTp0gI6ODvT19dG3b1+oqanB3t5e5LgNlYVdl/Zb16ElqmrevDn279+P5cuX4+bNmwgODsbLly/Z8dTj4uJw8OBBeHl5wcPDQ2ioiJqo7nsveI783w7BZVu2bEHfvn1lOpakbN66XDt59XHy6qunTp2Ke/fuobi4GLdv38akSZNQXFyMW7duAQAmTJggdjI7QgghhBBC+CjoS0gN/fbbbwgLC0Nubi4uXboES0tLqdvLmhFYUFAgtzrWhWD2rjiCr28LZtHyM6Py8/Oho6NT61ds5YFfl4qKCuTk5Egd85DH47FZY+ImqPoWcDgcrFq1CkVFRVBUVMTixYthaWkJHR0dkUCFpLGaZQ1oSGqHgkOGCAYQxamuDQHSvws8Hg8lJSXV7kOQ4L0rLS2t9vXs/wWlpaVYtmwZe7/mzJmDqVOnQk9PT+QBkqR2I08KCgoYNGgQO+Z4amoqQkNDERQUhPv376OsrAxXr16FmZkZO3a4t7c3G/Dt0qULli9fDjMzM5FMzCdPnog9pmC7+JqZ/IL1q+77UnWb+spI7ty5MxYuXIiFCxfiy5cvePbsGUJCQhAQEICsrCykp6dj8+bNcHd3r9X+8/LypA41wp8AtUmTJuwbMoL3R1FRsUG/t/Lo4+TRV/Pxx+vNzs5GQEAAJk2ahODgYLac4Pj6hBBCCCGEiEMTuRFSQ61bt8aGDRvYf2/evBnFxcUStxcMrkgLZPHH92to79+/lzqZU1RUFPtZcIxM/sRY5eXlePr0qdRjPH78GK6urggMDGQDAfIkOJ7ry5cvpW77+vVrNuNNcNKjb8nVq1dRWFgIAFi6dCmWL18OXV1dkSACl8sVekVZkGBmmrT2+vHjR7HLdXR02LYsafI9PsHxnSXVQ97fhc6dO7MZu0+fPq0209LV1RXnzp3D48ePa3ysf4ugoCD2fk6ZMgWbNm1Cjx49xL4xIOm+y0NhYSGioqJEXnPv1KkTpk+fjmPHjgmN5SqYcezl5QWgsh/9559/YGlpKTYoKqnNdOrUiR0SJzY2Vmo9z549CwsLC8ydO5cd47i2OnfuzL6OX10fBACRkZHs565du9bp2ILKysqQlJSE58+fCy1XVVWFubk5Nm3ahBs3brDjGYeFhUntH6SR9r1nGAavX78GAKHJ4vi/G4DkwD1ffn4+jhw5gsuXL+Pt27e1qqM08ujj5NFX8ykpKWHChAkAKn8zi4uLcffuXQCVQ3IYGBhUf1KEEEIIIeR/GgV9CamFyZMnY+jQoQCAtLQ0XLt2TeK2gmP+Spo0Kjs7WyiY2pBKS0vZPyzFuXjxIoDKzFHB2eQFP0vLFGMYBjt27MC+ffuwfPnyWgcYpBGsi4+Pj9Rtvb292c9mZmZyr4s8vH//nv0s7Q/9wMBAlJaWAoDIZFpqamrs57S0NIn7ePDggdjlGhoa7ORSwcHBEjPhuFwuO1aoOPx6fP78WWLg9/79+xLLS6KkpIQBAwYAADIyMnDz5k2J20ZERGDfvn3YsmULTpw4UeNjVUcer+fLYx/84RMA6e3mxYsXQn1T1bZTF58+fYKxsTGmT5+OI0eOSNxuyJAhQpPN8fHbfosWLaROtCbYBwvWX0lJCT/88AMACE2eJc69e/eQlpaGyMhIofFpa3MvGjdujIEDBwKofLAUHR0tcdv3798jNDQUQGVAVJYxgGW1YMEC/Pjjj5g3b57EzNKWLVsKTXgp7aGfNPyxZsV58OABMjMzAQCjRo1ilxsaGrJB/Fu3bkm9P97e3vjrr7+wfv163Llzp1Z1lEYefZw8+mpBU6ZMAVD5nQgODmb7Z8ryJYQQQgghsqCgLyG1tH37dnbSGH6mqDiCWac+Pj4i25aVlWHr1q1S9/G17dq1S2zm3+XLlxEYGAgAGDZsmFBG2qhRo9igzIMHD/D333+L3ffBgwfZyZxGjBght5njBfXs2RMmJiYAKrMdPTw8xG7n5+cHPz8/AJVZXpImkWpogsNoSArKRkVFYefOney/qwZumjVrxt6fFy9e4M2bNyL7uHPnDjtepDh2dnYAKjOF165dK3IMhmGwc+dONrgjDv/7wOPxcO7cOZH1ycnJOBxUyJkAACAASURBVHbsmMTy0sybN4/9vGPHDrx7905km7y8PGzcuJH999y5c2t1LGkEJ1ar7bAJ/H18+fKl1vWQpd0kJydj7dq1QstqG/QTp3379mxmZ0BAACIiIsRud/36dTY729DQkF3OP4ecnByxD8YYhoGzszMePnwosf788Z3Ly8vx66+/ij2/gIAAdh+WlpZsli7w//eipvdSsD2uXbtWbGZnXl4eVq1axY5t6+DgUKNjVIffp5WWluLQoUNit8nKykJYWBgAQFtbu9bD3ISGhgo9ROPLyMjAtm3bAFQGmPmBTKDy2tra2gKovG8rV64UO8RMdHQ0+4CmSZMmmDFjRq3qWJ269nHy6KsFde/enX2j5o8//kBeXh4aN27MZgATQgghhBAiDY3pS0gtdejQAatXr2b/mJWkb9++0NbWxocPH/Dq1SvY29vDzs4OmpqaSExMxNmzZ/H69Wt2m4bWtGlTcDgcTJ06FQsXLoSRkRGKiopw/fp1XL58GUDlmJNbt24VKte4cWPs27cPdnZ24PF4OHDgAJ4+fYopU6agQ4cOSE9Ph6+vL5vF2bJlS/z666/1dh67du3C1KlTkZ+fj127diE0NBSTJ09Ghw4dwOFwcP36ddy4cQNAZRDh8OHDcpmArD78+OOPOHnyJBiGgZeXF4qLizFmzBhoaGggIyMDd+/exfXr14UeHBQWFoJhGKEsxcmTJ+PIkSNgGAbz58/H4sWL0bt3bxQUFCAwMBCXL1+GpqYmCgsLxWZgW1hYYPz48fD398fDhw8xdepUzJ8/Hzo6OkhPT4eXlxfCwsLQrFkziQGySZMm4dKlSwCA/fv3IyMjg52BPjw8HJ6enigqKkLHjh2lZiSLM3DgQNjY2MDb2xvZ2dmYPn06bG1tYWZmBiUlJcTExMDV1ZV9oDFq1CiMHDmyRseQhWCW6LFjx+Dg4ICKigqhbMrq8McXzc3NxcmTJ2FqaoqmTZvi+++/l3kfw4YNQ9OmTVFcXIzg4GD89NNPmDJlCjQ1NZGVlYWHDx/Cz89P5F4VFBQIBT3rasWKFVi6dCm4XC7mzZsHa2trmJiYoE2bNsjKysKDBw/YNtGqVSvMmjWLLTt27Fi4ubkBAJYsWYIFCxbAwMAAjRo1Qnx8PHx9fUVexee/Xi94Hfjt9vHjx5gyZQrs7Oygp6eH7OxsBAcHs+MGt2rVCqtWrRIqr6mpidTUVISEhODmzZvQ0tJCu3btJE5CyWdiYoI5c+bA09MTCQkJmDhxIuzs7GBsbIxGjRrh1atXOH36ND59+gQAGD9+PCZNmlTzCyzF9OnT4eHhgbS0NHh6eiI+Ph5TpkxBp06dwOVyERsbCw8PD3A4HADAzz//XKfjbdu2DZGRkRg/fjzU1NQQGRmJU6dOsVmza9euFclkXrx4MYKDg/H69WtERkZi4sSJsLe3h6GhIYqLixEeHg4PDw+2na5evVroOyZPde3j5NVXC7KyssKbN2/YIUfMzMzq7fwJIYQQQsh/y7cZ4SDkX8LGxgYBAQEIDw+XuE3jxo2xf/9+LFiwAAUFBXj27BmePXsmtM2IESMwefJkLFu2rL6rXK0ePXqgf//+OHnyJPbt2yeyvkOHDjh58iTat28vss7Y2BinTp3CqlWrkJubi/v374t9Vb99+/Y4evQoOnXqVC/nAFRmrHl6euLnn39Gamoq7t27h3v37ols17lzZxw+fFhonMlvTY8ePbBy5UocPnwYDMPA19cXvr6+ItuZm5ujXbt2OH/+PHg8Ht6/f48uXbqw6+fPn48nT54gPDwc2dnZ2LNnj1D5tm3b4tSpU7C3t5c47MaePXvQqFEjXLt2DXFxcVi3bp3Q+o4dO8Le3h67du0SW37AgAFYsGAB/vnnH5SVlcHV1RWurq7semVlZezatYt91b6mNm/ejCZNmsDd3R1FRUX4+++/xWadjx49Gvv376/x/mVhamoKVVVVfPnyBQEBAQgICICSkhJevHghlAUszejRo9l7fOjQIRw6dAj9+/fHmTNnZK6HpqYmtm7dil9//RXl5eW4e/eu2KFb+vTpg4EDB+LkyZMAgHfv3lUb0KwJCwsLrFmzBocOHUJxcTHc3NzYQK6g9u3b49ixY0Jj9i5btgzPnj1DdHQ0Pn/+LLZPUlJSgpOTE06fPo2MjAzEx8eLbLNnzx4oKCjg6tWriI+Px6ZNm0S20dLSwrFjx0QCaqNHj4arqyuKioqwYsUKAJXB0eXLl1d77r/++iuUlJRw+vRpfP78WWy2baNGjWBnZ4fVq1dXu7+aUlVVxfHjx7Fw4UJkZGQgLCyMzeoVpKioiOXLl9dp2ABbW1v27Qn+GxSC+//tt98wbdo0kXJNmjSBi4sLVq5cibCwMHz8+BG7d+8W2a5x48ZYvnx5vWTnC6pLHyevvlrQhAkT8Pvvv7OBYisrq7qfJCGEEEII+Z9AQV9C6qBRo0bYuXMnJk6cKHViKiMjIwQEBMDV1ZWdXKlp06bQ19fHtGnTMHHiRAQFBX3Fmkv3yy+/oHfv3nB3d0dMTAzKy8uhra2NMWPGwM7Ojh3WQhxTU1PcvXsX3t7eCA4ORkJCAps5+P3332PEiBGYOXMmmjdvXu/n0b17dwQEBODChQu4ffs2YmNjUVBQgNatW6Nr164YP348LC0t2QnAvmVLlixB79694enpiaioKOTm5kJJSQmampro2bMnpkyZAnNzc4SGhuL8+fMAKl9ZX7p0KbuPpk2bws3NDb6+vrhy5QpiY2PB5XLRsWNHjBo1CvPmzRM7SZYgZWVlHDhwAJMnT8aZM2cQExOD7OxstG/fHiNHjsTSpUurnRxtzZo1GDx4MLy8vPDixQvk5eVBU1MTAwcOhIODA7p16yY2QC+Lxo0bY8OGDbCysoK3tzfCw8Px6dMn8Hg8tGrVCkZGRpg6dSqbXVwf2rZti9OnT+Pw4cOIjo5GaWkpNDU1kZ6eju+++06mfQwfPhz79u2Dm5sbkpOT0ahRI6GxbmU1efJk6OjowM3NDc+ePUN2djYUFBTQunVr6OvrY8KECRgzZgw+fvzIBn0DAgLkPr71ggULMHjwYHh7e+P58+f4+PEjSktLoa6uDl1dXYwYMQIzZswQ+S42b94cXl5e8PT0REBAABITE1FaWgpVVVV07twZAwYMwKxZs9C5c2e8efMGV69eRVRUFNLS0oSGjlFWVsb+/fsxbdo0nD9/Hi9evEBWVhYUFRWho6OD0aNHw9bWVmy/tGrVKigrK8Pf3x8ZGRlQVVUVOwSBOAoKCli3bh0mTZoELy8vPHnyBBkZGVBQUICWlhYGDBiA6dOn1+tDJ319fdy4cQPnzp1DcHAw3r17h4KCAjRt2hTt27eHqakprK2toaurW6fjGBsbw8HBAceOHUNISAjy8vKgpaWFQYMGwd7eXmrb19DQgLu7O+7du4erV6/i5cuXbHZwhw4dMGDAANja2goNl1Rf6trHyaOvFqShoYFBgwbhwYMHaNGiBUaMGFEv500IIYQQQv57GjEMwzR0JQghhBBCyL+Lr68vNmzYAKAyI93S0rKBa/TfU15eDnNzc3A4HFhbW2P79u0NXSVCCCGEEPIvQRO5EUIIIYQQ8g16+PAhO+ayuOExCCGEEEIIkYSCvoQQQgghhHxj8vPzcfjwYQBAr1690Lt37wauESGEEEII+TehMX0JIYQQQgj5Bty7dw9XrlxBs2bN2IntAMDR0bGBa0YIIYQQQv5tKOhLCCGEEELIN0BRURE3b94UWjZlyhRYWFg0UI0IIYQQQsi/FQV9CSGEEEII+Qbo6OigS5cuSEtLQ7t27TBt2jQsXry4oatFCCGEEEL+hRoxDMM0dCUIIYQQQgghhBBCCCGEyAdN5EYIIYQQQgghhBBCCCH/IRT0JYQQQgghhBBCCCGEkP8QCvoSQgghhBBCCCGEEELIfwgFfQkhhBBCCCGEEEIIIeQ/hIK+hMjZkydPoK+vD319ffj6+jZ0db5Zc+bMgb6+PgwNDRu6Kt8UHo+H48ePY9y4cTAyMoKxsTF+/PFHREREyO0Yvr6+bBt98uSJ3PZbVWpqKgoLC+tt/3WVnZ2NzMzMBq0D/3tgYWFR6308evQI9vb2GDhwIAwNDTF8+HAcOHBAjrX8tlhYWEBfXx9z5syR+77Xr1/Pfjc4HI7c9y9Pb9++rVN5LpeLhIQEOdWG1IWkvkiwr75+/XoD1IwQQgghhPybUdCXEEK+IWvWrMEff/yBhIQEFBcXo7CwEElJSWjbtm1DV01mXC4XR48ehaWlJXJzcxu6OiIqKipw9uxZ/Pjjj0hKSmro6tRJcHAwFixYgNDQUOTk5IDL5eLjx49QUVFp6KqRevL+/XssWLAAu3btqvU+Hj9+jIkTJ+LGjRtyrBmpqf9SX0QIIYQQQr49ig1dAUIIIZUSEhIQEBAAAOjYsSOcnJzQqVMn5OXloWPHjg1cO9n9/fffcHZ2buhqSHT16lVs3769oashF8eOHUNFRQUAYNmyZRg0aBDKysqgra3dwDUj9WX+/PlISUmBiYlJrcqnp6dj3rx5cq4VqY3/Ul9ECCGEEEK+PRT0JYSQb8S7d+/Yz0uWLIGlpWUD1qb2+EHIb9W3Xr+aiI+PBwD07dsXjo6ODVwb8jXUtf2Wl5fLqSakrqq7l1OmTMGUKVO+Um0IIYQQQsh/DQ3vQAgh34ji4mL2c6dOnRqwJuTfgt9mqL0QQgghhBBCCBFEQV9CCPlGCGZ9NW7cuAFrQv4tGIYBACgq0os7hBBCCCGEEEL+H/2VSP5VCgsLcfHiRTx8+BBxcXHIy8sDwzBo2bIlevbsiVGjRmHSpElQUlISKWthYYG0tDTMnTsXGzduRGhoKLy9vREREYGcnByoq6ujX79+mDVrFgYOHCi1Hs+ePYOHhwdev36NzMxMaGpqYvjw4Vi0aFGdzi81NRUjRowAABw9ehQWFhbw9fXFlStXEB8fjy9fvqBdu3YYPHgwHBwcxI7beeTIEfz1118AAB8fHxgZGYk91vbt23H27FkAwN27d4UyBefMmYPw8HCMGTMGzs7OeP78Odzd3fHixQvk5+cLnS9/grEPHz7AxcUFISEhyMzMhJqaGoyNjbF48WIYGhpWe+5Pnz6Fi4sLIiIiUFJSgvbt28Pc3Bz29vbQ0tKSWra4uBje3t64c+cOEhMTUVhYCHV1dRgYGGDChAkYO3YsFBREn3E9efIEc+fOBQD4+fkhMzMThw8fRkJCAlRVVaGvr4/ff/8d7dq1q7b+fFwuF35+fggMDERMTAzy8/OhpqaG77//HiNHjoS1tbXIJFv86y2IXy8A8PDwwIABA2SuA8MwCA4Ohre3N+Li4pCdnY0OHTpgzJgxWLhwocz7CQkJga+vLyIjI5GVlQUVFRVoa2vD3Nwcs2fPRqtWrYS29/X1xYYNG4SW8dtzx44dce/ePZFjREZGwsfHB0+fPkVmZiYUFRXRsWNHDB48GHPmzKn23nO5XNy8eRNXrlxBYmIiOBwOmjdvju7du2PixImYPHkye+8F7zef4L9jY2NF9p+QkIAzZ84gNDQUGRkZYBgG7du3x4ABAzBnzhx8//33UuuXn5+Pc+fO4ebNm0hJSQHDMOjevTtmzZqFcePGSS0ribjrfPnyZVy+fBkAYGVlhb1799aqfT979gzXrl3DixcvkJmZicLCQqiqqqJdu3YwMTHBrFmzoKurK1InefQ7fBwOB56enggODkZKSgqUlZVhaGgIe3t7DB48WOJ1ETxfJycnif3xzZs3sWLFCgDAnj17avz6PMMwCAgIwLVr1xAdHY2cnByoqqpCV1cXI0aMwMyZM6Gqqiq2rL6+PgBgw4YNsLe3x61bt3DhwgW8efMGeXl5aNOmDQYMGAA7Ozv07NlTqGzVfiI8PJzdn6OjI5YtW1Zt3fnb8/3111/sfeP3M1V/hzQ1NbFnzx68efMGKioq0NXVxZYtW9C9e3d2P2/fvoWvry+ePXuGjx8/orCwECoqKmjTpg2MjY1hbW2N3r17i9SnahvV1dWFl5cXbty4gaSkJHC5XHTs2BHDhg2Dg4MD2rRpI/a8ysrKcPXqVdy4cYO9lqqqqtDS0sLAgQNhY2OD7777TuJ1KS0txZUrV3D//n3ExMQgJycHPB4PLVu2RLdu3TBs2DDMmDEDzZo1k7iP+uiLBL/rhw4dEjvkT3l5OQIDA3Ht2jW8evUKubm5UFVVRZcuXWBubg5bW1u0bNlSbJ3l9f+iV69esf14eno6FBQU0KpVKxgZGWHcuHEYMWIEGjVqJLE8IYQQQgipHxT0Jf8aISEhcHJyQl5ensi6zMxMZGZmIjg4GOfPn4erqyuaN28ucV/79+/HP//8I7SMw+EgMDAQgYGBWLp0KVauXClSrqKiAjt27ICXl5fQ8rS0NJw5cwbXrl2rUVBNmuLiYtjb2+PJkydCy1NSUuDt7Y1Lly7hr7/+grm5uVyOJ8mJEyfwxx9/sBmFQGVw2tPTE3fu3IGPjw/evHkDJycnfPnyhd0mOzsbt2/fRnBwMI4fP44hQ4ZIPIabmxv27t0rdIzk5GQkJyfDx8cHe/fuxdixY8WWjYqKgqOjIzIyMoSWczgcBAUFISgoCJ6ennB2dmYD1OLcv38ff/75J5tty+VykZaWJrVMVW/fvsXKlStFZmHPzs5GeHg4wsPDcfr0aRw5ckSmQHhtlJaWwsnJCbdv3xZanpycjJMnT+Lq1auwsrKSuo+ioiKsXbtWZB9cLhfR0dGIjo6Gu7s79u/fDwsLi1rVs6ysDDt37oS3t7dI/ePi4hAXF4czZ85g06ZNsLa2FruP5ORkLFu2DHFxcULLc3JyEBoaitDQUFy4cAEnT55EixYtalzHY8eO4a+//hIZAzUpKQlJSUk4f/48fvrpJzg6OooNaMTExGDhwoXgcDhCy58+fYqnT58iKCjoq40vXF37Likpwdq1axEYGChSNi8vD3l5eYiLi8O5c+ewZ88eTJw4sV7q+ejRIyxbtkyoLykqKkJISAhCQkKwZMmSejmurD5//gxHR0e8ePFCaHlubi6eP3+O58+fw83NDc7Ozujbt6/E/VRUVMDJyQn+/v5Cy9PT0+Hn54erV69i27ZtmDFjRr2ch6zevHkDFxcXlJSUAKj8fr59+xadO3cGUBlw3L17N86ePSvUfwMAj8dDQUEBkpKScPHiRamBeKDy2q5du1bk+5yQkICEhARcuHABp0+fhoGBgdD6goICLFq0SOSe8NttTEwMPDw8sHHjRtja2ooc9/Xr1/jpp5/w6dMnkXVZWVnIyspig6EeHh5iHwLWd18kSVpaGlatWoWXL18KLc/NzUVkZCQiIyPh5uaGAwcOYOjQoVL3Vdv/F/399984ePCgyP1PS0tDWloarl+/DlNTUxw9elRq0JwQQgghhMgfBX3Jv0JiYiKWLl0KHo8HFRUVWFtbw9TUFBoaGsjKysLLly/h6emJoqIivHz5EidPnoSTk5PYfQUEBIDD4aBDhw6wt7dHnz59UFJSglu3bsHb2xsMw+D48eMYNmyYSLba/v372YBvx44dsWjRInTv3h25ubnw8/NDQEAADh06JJdz3rdvHzgcDoyMjDBr1ix07doVmZmZ8PLywqNHj8DlcrF+/XrcuXNHYlZZXYWHhyMwMBDt2rXD/PnzYWBgAA6HgxMnTiAmJgbp6elYvXo1oqKi0KRJE6xatQr9+/cHl8vFxYsX4e/vDx6Ph23btuHWrVtis225XC727NkDFRUV2NvbY8iQISgrK8OdO3fg7e2NkpISODk5QUtLC3369BEqGx8fDzs7OxQVFaFp06awsbGBmZkZWrRogfT0dFy/fh2BgYGIiIiAg4MDfHx8JF6rP//8E2pqalixYgV69OiBuLg4KCsry5ydlJycjHnz5iE7OxsAMHz4cEyePBlaWlrIzMyEv78/AgICkJ6ejrlz58LHxwd6enoAgJ07d6KoqAh3797FkSNH2GX84Ia4jG5J1qxZwwZr9fX14eDggK5duyIjIwPnzp3Do0ePcOLECYnlKyoqsHTpUoSFhQEABg8eDCsrK2hra+PLly8IDw/HmTNnkJ+fD0dHR7i4uGDQoEEAKrPG/Pz84O3tDR8fHwDAqVOn0LZtW5Hs+02bNrGZqX369MGMGTPQrVs3cLlcREZGwsPDA5mZmfjtt9+grKwsEqjOzs6GjY0Ne70tLCwwefJktG/fHqmpqXBzc0NUVBRevHiB1atX49SpUzAwMICfn5/E6yxIMHNVV1cXtra26NGjBwCwQaTk5GR2m6pZlhkZGZg9ezYKCwvRqFEjWFlZYdy4cWjevDlevXqFU6dO4erVq2K/E9XhX2cAmDx5MoDK9sbPXhWX1Vdd+96yZQsb8DUwMICNjQ20tbWhoKCAlJQU+Pj4ICIiAmVlZdiyZQuGDBkCDQ2NGtddmjdv3mDx4sXg8XhQUlLCrFmzYGFhASUlJYSHh8PFxQUnTpyo1TWTh6KiIsydO5edcHHcuHEYO3Ys2rdvj/z8fDx48AA+Pj7IzMyEg4MDzp8/j27duondl6urKzgcDnR1dWFvbw99fX3k5eXh8uXLuHHjBvuA0czMDB07dgTw//0E/0FCr169sGvXLgCQmAFbFT/jmx98tba2ho2NDQDx/czx48ehpKQEJycn/PDDD/jw4QOys7PZfvTo0aM4c+YMAKBr166YM2cOdHR00KRJE6SlpeHatWu4f/8+AODw4cOwsLCQmB2/fv16cDgcDBkyBNOmTYOWlhZSU1Ph6uqKV69eIT8/H+vWrYO/v79Qv7xnzx424Dtt2jSMGjUKrVu3Rl5eHsLDw9n/G+zcuRP9+vVjv8dAZVDWwcEBubm5aNy4MaZMmYJhw4ahTZs2bMDYzc0NOTk5SE5Oxr59+0R+4+u7L5IkOzsbDg4OSE5OBgA2o7pLly7Izc3FnTt3cOnSJeTl5WHJkiX4559/YGpqKnZftf1/0dOnT9mAb/fu3WFnZ4euXbuioqICiYmJcHNzw7t37/D48WM4Oztj/fr1Mp8fIYQQQgiRA4aQfwEnJydGT0+P0dPTY27fvi12mzdv3jA9e/Zk9PT0mJEjR4qsHz58OLsPS0tLJjc3V2SbU6dOsdv89ttvQuvevXvH7n/8+PFiy7u6urLl9fT0mEuXLtXoPFNSUoTKr1mzhikvLxfapqKiglm4cCG7zfXr14XWOzs7s+siIiIkHmvbtm3sdikpKULrZs+eza4bPHgw8+nTJ6H12dnZTO/evdltfvjhByYxMVHkGMuWLWO3iYmJkXgMIyMj5uXLlyLl7969y+jr6zN6enrMlClTRNZPmjSJ0dPTYwYNGsS8e/dO7Hn6+Piwx9m/f7/QurCwMKHr/fDhQ/EXSwb29vbsflxdXcVu4+vry24zYcIEpqKiQmj9pUuX2PVhYWE1rsPDhw/Z8nZ2dkxpaanINjt27BA656rHOX36NLvOxcVF7HFSUlKYwYMHM3p6eoy5uTnD5XKF1gu2wapti2EY5vbt2+z6HTt2iLRxhqlsY+PHj2fbR3Z2ttB6wT5BXD15PJ7QPXn69Cm7rrrrHB0dzXTv3p3R09Njli1bJvY6FhcXM3Z2doyenh6jr6/PxMfHC63/5Zdf2GOcP39epDyHw2FGjx7NbjN8+HCRbWTBL79u3TqRdbK276SkJPZ7Nn36dLHnyzDC32c/Pz+hdfLod6ytrRk9PT2me/fuzIMHD0TKJiQkMAMGDGDLz549W+L5njx5UmIdAgICJPbR69atY9dlZmYKrdu5cydbvxs3bojd9+vXrxkjIyP2WlYleD/s7OyYkpISkW1+++03qefB/y2rev6yEvydcXZ2lrpeT0+P8fHxEbufgoICxtDQkNHT02MsLCyYnJwcsdvt3buX3dfRo0eF1lVto4cOHRIpX1paykycOJHdJjIyUmidgYEBo6enx2zcuFHs8YODg9my27dvF1p36NAhdt3p06fFlv/06RNjbGzM6OnpMYaGhgyPxxNaX599keB6f39/oXUbN24U6ker/p4wTOVvQq9evRg9PT3G1NSUKSoqElpf1/8XrV+/ntHT02NMTEyY/Px8kbIFBQXMyJEjGT09PcbY2JgpKysT2YYQQgghhNQfmsiN/CtkZ2ejVatW6NmzJ0aOHCl2mx49erAZRFVf9a9qzZo1YrPhZsyYwWYQVR3f8+LFiygrKwMAbN26VWz5efPmoX///tWfkAyaNGmCX3/9VSSrrVGjRkKv/L59+1Yux5Nk0aJFIq+zamhowMTEhP333Llz0bVrV5Gygq/+v3//XuIxHB0dxY73aGFhwWZ4RkdH49WrV+y6R48eISYmBgDwyy+/iB1nFKi8p/zsJm9vb/B4PLHbaWtrw8zMTGIdpYmJicHjx48BAMOGDcO8efPEbmdlZcWOHxobG4sHDx7U6niS8LNrGzdujN27d0NZWVlkm3Xr1kkc27KiogJubm4AgB9++AEODg5it+vUqRPWrFkDoPJ19KrDQFSH/wpxp06dsH79erGZmxoaGti6dSuAygzLCxcusOsKCgpw8+ZNAED//v3F1lNRUVEoqywkJETm+rm6uqKiogLNmjXDrl27xF5HFRUV7N69GwoKCmAYBp6enuy6/Px8tn6mpqaYPn26SPk2bdpg27ZtMteprqS177i4OHz33XdQVlbG4sWLxZ4vAKEhHarrY2sqLi4OERERAICpU6eKHQ5GR0eHbXdfW35+PtsG+eOEi9OzZ082i/bly5cir90L2rRpE5o0aSKyfObMmexnceNMf00qKipsNnlV8fHx6NSpE5o2bQo7Ozuoq6uL3U7WdqOpqSl2XOKqmf6Cv3n5+fngcrkAILFfMzc3x5w5c+Do6CgyxAF/TH5NTU2xQz8AYMezBiqHt8jNzWXX1XdfJEl2djb7poS+vj42bNgg9q0UGts1TwAAIABJREFUMzMzdkiUrKwstow4tfl/EX/omtatW0NNTU2kbPPmzbFy5UrMmzcPq1atQmlpqYxnSAghhBBC5IGCvuRfwdXVFaGhobh06ZLU7fivuPL/CBRHUVFR4oQkLVu2ZP/oERxTEgD7iqqWlhaMjY0l7n/q1KlS6yirXr16Sfwjmj+eIiBaT3mT9DqoYCCY/3p/Va1bt2Y/FxUVid1GQUFBbFCMTzDg8OjRI/ZzcHAw+7m6YC1/3OPCwkKhwLGgqkNH1ITgH/GSxp/l479KXbVcXZWVlbHXp1+/fhInQFNSUpI4HmtsbCzS09MBSL7vfEOHDmUDAaGhoTLXMy8vD5GRkQCAAQMGQFFR8ihD/fr1Y8e/5A83AVReN37wftKkSRLL6+vrw8/PD8+fP8eqVatkqh/DMOx96d27t9hABp+Wlhb7+r5g/R4+fMg+IBo/frzE8gMHDhQ7kVl9kNa+R48ejcDAQERFRUkdo1lwCAFpfWxt8PtXQPo1s7S0FJkI8WsIDw9HcXExgOq/G4LjrEv6brRr107iMAdfs3+vTs+ePSU+BOjbty9u3LiByMhIzJ49W+I+ZG03AwcOlNgfCA49IXhNWrduzf5Onjx5EpcvX2bHHxa0adMmLFu2TGQM/D179uDhw4d48OCB2AlgqzuH+uyLpAkLC2P7mOnTp6Nx48YStxX8zZH0oLG2/y/S0dEBUDnu8qZNm5CWliZS3tLSEuvXr4etrS2N6UsIIYQQ8pXRmL7kX4WfEVhaWorU1FSkpqYiKSkJsbGxePHiBTu2HVNlQhFBbdq0EZtdxdesWTPk5uayf1ABlRmQ/H0LzlgujriM1dqQFgwS/MNJsJ71gT+eZFWCgQBNTc1qt5F0T7p06SJ1YhvBGewFJ8nhZ/kCldm1skpJSUG/fv1Elnfo0EHmfVQVHx/Pfq46DnRVPXv2hJKSEng8nsikP3WRmZmJwsJCANW3UUmTyL1584b97OzsDGdnZ5mOnZKSImMtK+8bvy1cunSp2gc54o4hOFFer169pJYTHL9TFqmpqexkkWFhYdDX15e5HF9iYqLMxzc0NBQqW19kad+CmYJZWVlISUnB+/fvkZCQgOjoaDYTF5Dex9aGrNdMRUUFenp6iIqKkuvxqyP43Vi3bh3WrVsnUzlJ3w1p/bvguOP13b9XR9Z+kf/bnJOTg5SUFKSkpODdu3d48+YNnj9/zm4nrd3U5jevUaNGWLBgAQ4cOICCggKsX78eW7ZswQ8//IBBgwZh8ODB6N69e7Vjs/Prz+Px8PHjR6SkpCA5OZnNQBfsqwUnX6zPvkgawfpU98CydevW6Ny5M1JSUoR+qwTV5v9FAGBra4uLFy/iy5cvuHDhAi5cuIBu3brB1NQUpqamMDExoUAvIYQQQkgDoqAv+dfIzc2Fu7s7AgMDkZSUJHbWewUFBbHLBTVt2lTqev4fh4J/nObm5qK8vBwAJGbf8glmt9aFtHoK/gEr7+CLoMaNG8uUVScty6g61V0vNTU1NkjKD8YBlcGF2sjPzxe7vHnz5rXaHwD2dV8FBYVqJ7dSVFSEuro6OByO0GvCdZWVlcV+rq6NSpr0Sd7XtL6O8fnzZ/ZzdedaU7WtX1lZGQoLC9G8efMa1U/WCbjqSpb2HRISAi8vLzx79kzsPa3PCdT410xBQUHsK+aC5NXH1oS8vxvfQv8uC1nazcuXL+Hh4YHHjx+zk5kJkrXdSAsOSgvaLly4EBUVFTh27BhKSkpQWlqKR48e4dGjRzhw4ADatm2LMWPGwN7eXmxgubi4GF5eXvD390dcXJzYQLuk/1vUZ18kjeBvhyzfhzZt2iAlJUXib05t/l8EVA6p4eLigl9//ZV9cBMfH4/4+Hi4u7tDWVkZZmZmmDlzZo0ezhJCCCGEEPmgoC/5V4iOjsaiRYuE/sBSUVGBjo4OdHV1YWBggIEDB+Lw4cNCr/03BGmvq39LqguOA3UL5soT/w9Nwddv+X+Ya2ho4PTp0zLvq+r4xHzVZYJJI8u1FLe9PINoNam/pDbKf7ABAFu2bEHfvn1l2p+0DDFpx1i6dCnGjBkjUznBa1Wf2Y+C9Zs6dSrmzJkjc1l+4EQe90LepNWJYRhs3rxZaNxkoDLLU0dHB/r6+ujbty/U1NRgb29fp3pI+q5UDXRKq6+01/DrUgdpBNvF0aNHJb4BUVVdHib9Gxw9elTkjYA2bdr8H3v3HRXF+TVw/Is0UayogLH3rmgSCypWBAuiaOxRicYGtsSu+ZnEEiOWqCFqVOwNwdiioCJ2sGIBLGClKRYEFUGQ9w/Ozsu6S1MQNfdzTk5k9pmZu7NTdu88cx9lv6lfvz61atWiY8eOuRrHsGHD6N27NwcPHsTHxwc/Pz+lFMHDhw/ZsGED27dvZ/HixWolTO7fv893332nVnNeX1+fihUrUqlSJWrXrs3XX3/Nrl272Lx5s8Z686ondtp9OCvnG9X+mxs3biwsLNi3bx/+/v4cPHiQ48ePc+/ePSC1FMaRI0c4cuQIdnZ2zJs3L1dvHgkhhBBCCHWfRnZK/KclJCTg7OysJHwHDBiAg4MD1apV00hKplc39n0VK1ZM6W2qrSdTWml7o+aFrPYSi4uL+xDhZCqz7ZX2kdK0vWhVvapiY2OpVKlSthKPOU0Vy5s3b3j69CnFixdPt+3r16+V3n+Z9WjMjlKlSin/ftd9NG08enp6Ofo4srZ1pKSkvNM60vaoi4mJSbd+8btIG19CQsI7xZe23Mnjx48zjC+vzxeQOsChKuFboUIFRo8ejaWlpUbPRX9//3SX8b7nHdU2y8oxlF5vxazGoCqDkh1p9wsjI6NcOTY+NUePHlUSviVLlmTMmDFYWVmpnYuAD1K+BFI/ox49etCjRw+SkpK4cuUKJ0+e5MCBA9y8eZOEhAQmTZrEkSNHlGT8Dz/8oCR8O3fuTP/+/alTp47GjYWtW7dqXWdunosykvZa+OjRo0zXq/r+lJPXnLTy5ctH06ZNlfr+YWFhnD59miNHjnD06FGSkpLYvXs3lpaW6Q4MKIQQQgghcp7cbhcfvSNHjhAREQFA9+7dmT59OjVr1tTaC1XVLqfp6OhQuXJlAAIDAzNMKKStNZsX0m4XbQPaqKgG7Mprd+/ezXBwn7S1O9PW91UNoJWcnMzZs2czXMepU6dYs2YNXl5emSZE30Xauq+XLl3KsG1gYKAy8I9qEJycUKJECSUBkd5gdSrp7aOqbQoZJ/ggNdm+dOlSdu7cybVr17IcZ7Vq1bK8juTkZJYtW4a7u7taPVnVsQhkuu6+ffvSuXNnfvrppyzFV7ZsWaXH7tmzZzPtFbpmzRq2bt3KqVOnlGlpt+O7fhYfkqoHo66uLqtWraJTp05aH1XP6JzxvuedtIOaZbTN3rx5k24t7LQxqAZd0+ZdrhPZOTbu37+Pq6sru3fvVmrBf47S9nxdtGgRPXv21Ej4Qu5fa6Kiojh9+rRar1s9PT0sLCxwcnJiz549yhMFsbGxSo3hK1euKOfrxo0bs2DBAiwsLLT2JE9vn8nNc1FG0l5zMqtv/fDhQ2WAtZy85kDqDZTLly9r1K4uU6YMPXv2xNXVVa0neF4/iSWEEEII8V8jSV/x0VM9JghQp06ddNtduHBBrUdRTj92aW1tDUB0dHSGP1x27dqVo+vNrrSDoqXXw+rJkycffCCk9CQkJHD48OF0X9+xYweQmnhv3ry5Mj3tv9etW5fu/CkpKfz666/MmzeP0aNHZ5gMeldpY9m2bVuGbbds2aL829LSMsdi0NXVpW3btkBqEuD69eta26WkpLB7926tr9WtW1dJ9nl7e2eYrNmyZQvLli1j8uTJHDp0SO21jB43NjU1VRJoFy9ezHA/3L9/P0uXLmX69Ols375dmd6kSRMlwbd3795053/w4AEXLlzg5s2bap97Ro8X6+vr07hxY2X+AwcOpNv24sWLzJs3j//9738sX75cmd68eXOlPqmnp2e6N4muX7/+USR9VT0dCxcuTNmyZdNtt2fPHuXfb59f3/e8Y21trew3GQ3u5+vrm25P37QxqJJc2hw7dizd19LTtGlTpRSHh4dHhr2F//77b/744w8mTJhAQEBAtteVmfcpRQM594h/2pIIGQ1ilvZ8k9PXZVdXV6ysrBg0aFC6N/90dHRo0aKF8rfqJmPa7xYZxR8WFsaFCxeUv9OW+sjNc1FGGjdurCSn3d3dM7w5lbaXcrNmzd5pfdpERUXRqFEjevbsydKlS9Nt16JFC7VBeIUQQgghxIcjSV/x0Uv7GGN6P9bv3LnDxIkT1aZl1Hv0XfTo0UN5JPSXX37R2vNnz549eHt75+h6syttD6Bt27YpvUpVkpKSmDlzpsb0vDR79myt23Pnzp14eXkB0KpVKypWrKi81r59eyVBdezYMf7++2+ty16wYIEywEzbtm2zXIszO2rVqsXXX38NpPZMX79+vdZ2//zzD//88w+Q2uMqpwe2GTBggJKAmDRpktZBpFasWMHVq1e1zm9gYEC/fv2A1ONn7NixWh/Hv3r1qpLkNDQ05JtvvtFYjoq2kiuDBw9W/j1hwgQePHig0SYsLIzffvsNSE3apK2ta2pqSrt27QA4efKkRi1aSO0R+r///U9JuPbs2fOd4vv1118JCQnRaPPs2TOmTZum/P3tt98q/86fPz+9e/cGUnsTLlu2TGP+58+fM3XqVI3peUF1jn369KnWpGxKSgpLlizhxIkTyrS3z6/ve94pXbq0cmPNy8sLT09PjTYPHjxg1qxZ6b6P8uXLK720Dx8+rPWmxdq1awkMDEx3GekpWbIknTt3BlIfp580aZLWa4yvr69yo6pkyZLY2Nhke12ZUe2/qpq17zo/vF9JpKxcm93d3dWOz5y+Lrdu3Vr598KFC7UmFd+8ecO///4LpCZZVU+MpI3/9OnTWvfN6OhoxowZo/Za2veQ2+ei9JiYmNClSxcgtYfx77//rrXd6dOnWblyJQDFixenW7duWV5HZszMzKhRowaQeoMu7dMYae3bt09JStetWzfH1i+EEEIIITInNX3FR69Vq1YYGRkRHx+Pr68vI0eOpHv37pQsWZJHjx5x4sQJ/vnnH40fTHFxcRmOBp5dpqamTJw4kZ9++omIiAi6d+/O0KFDadiwIS9fvmT//v14eHhQoECBXKstnBUWFhaUK1eOe/fuceXKFQYNGsTAgQMpWbIkt27dYtOmTQQGBipt8pqRkRHR0dE4ODgwdOhQGjRowMuXL9m3bx87d+4EUusmzpw5U20+XV1d5s2bx8CBA3n9+jUuLi6cPXuW7t27Y25uTmRkJJ6enhw9ehRIrWWYm0m22bNn4+DgQGxsLLNnz+b06dPY29tjbm5OdHQ0+/btUxIPhoaGLFq0KMcH8apZsyZDhgxhxYoVBAcHY29vz9ChQ6lVqxZPnz7F09MTLy+vDPfRYcOG4evrS2BgIAEBAdjZ2TFo0CDq1q1LfHw8Z86cYf369cr8P/74o8Yj3Wn/dnV1xdHRkTdv3tCgQQMgtUyLt7c3vr6+3LlzR1nHV199RVJSEpcuXcLNzY2nT58CMHDgQLXSHgDTpk3jzJkzPH36lBkzZuDv70+nTp0oVqwYd+7cYcOGDUpy287OTknKg3rN3TVr1lCkSBHevHlDo0aN0NHRoUmTJvTp04ctW7bw5MkTevbsSb9+/bC0tERfX5/g4GDWrFmj3Kho3769kvhRcXZ25tChQ9y7d49ly5YRGBhIz549KVGiBDdu3ODvv//m7t27eX6+ALC1tWXt2rUADB8+nCFDhlCnTh10dHS4efMmnp6eGiUX3u7pmhPnnenTp+Pv709MTAxTp07F39+fzp07U6hQIQICAli1ahXR0dHpbjMDAwNsbW3x9PTkxYsX9OvXjxEjRlC1alUePXrErl278Pb2fudz3+TJkzlz5gwREREcOnSI7t278+2331KtWjWePXvG0aNH2b59O8nJyejo6DBz5kzy58+f7fVkRrVNr1+/jru7OzVq1KBIkSKUK1cuS/OnrVG/d+9emjVrRuHChSlfvrzWsh7psbW1VXrATp06lZCQEBo1aoSBgQF3795l9+7dnD59Wm2ed6mnnJGaNWvSoUMHvLy8uHz5MnZ2dnz77bdUqlQJfX19wsLC2Lp1q5KQ7Natm3Ljr1GjRpQsWZLo6GiCg4MZNGgQ/fr144svviAmJoazZ8+yY8cO5Tyk8vaNsNw8F2Vk0qRJ+Pv7Ex4ejpubG1euXKFXr15UqFCBmJgYDh06hIeHB0lJSejo6DBv3rwcr+k7ZswYRowYQWJiIoMHD6ZXr158/fXXlChRgkePHnHs2DGl537x4sXp27dvjq5fCCGEEEJkTJK+4qNXsmRJZs6cydSpU0lOTubw4cNaywHUr1+fJk2asGLFCgBCQkIwNTXN0Vh69epFYmIic+bM4enTpxq9a/Lnz8/s2bMZN25cjq43O3R1dZk/fz5DhgwhLi6Oc+fOce7cObU2bdu2xd7eHmdn5zyK8v/VrFmTr776ihUrVjBv3jyN183NzVmxYgVmZmYarzVq1IiVK1cybtw4YmJiOHr0qJLkTcvMzIw///yTMmXK5Mp7AChXrhwbNmxg1KhRhIWF4ePjg4+Pj0a7smXLsmjRIqWHVE4bN24cycnJrFq1ivDwcI1kedGiRZk0aRJTpkzROr+hoSGrV69m7Nix+Pn5ERERwZw5czTa6erqMnr0aLUerirNmjWjYMGCvHjxgv3797N//3709fW5cOECBgYG6Ojo8McffzBt2jT27t1LTEwMixcv1hpPv379mDRpksZ0U1NTNmzYwPDhwwkLC2PPnj1q5QdUbGxsmD17ttq0unXrYmZmRlRUFGfOnKFPnz4AHDp0SOk9PmPGDAwNDVm3bh0vX77k77//1tqb3Nramvnz52tML1CgABs3bmTYsGEEBwcrI9inZWVlhbm5ebqDRH0ozs7OnDt3jqtXr/L48WOtx6G+vj4//PADbm5uPHjwgJs3b6q9nhPnnVKlSrFp0ya+//57wsPD1XrGq/Tq1YsHDx6kW2JnwoQJXL16lRs3bhAeHs706dPVXq9YsSLLli2jU6dOmW0WDcWKFVOO8WvXrnHz5k1mzJih0S5//vzMnDlT40ZATrG2tsbf35+kpCTl/dnb22v93LTR1dWlTZs2eHl58fDhQ4YMGQLAnDlzcHBwyHIcffv25eTJk/j6+vLixQutj/jny5cPR0dHzpw5w+XLlzX2m5wwe/ZsHj9+zLlz57hz5w6//PKL1nbt27dXOx8aGhoyf/58hg8fzqtXr7Tus5A6uGGPHj1wcXEBUr9bNGzYUHk9t89F6SlatCgbN25k1KhRBAUFpRt/8eLF+f3339VKXOSUNm3aMGHCBBYuXEh8fDxr165VbiClZWZmhqura7ZuKgghhBBCiPcnSV/xSbC3t6dSpUqsXbuWc+fO8eTJE/Lly4eJiQnVq1enS5cudOjQgYiICCXpu3///hytmaoyYMAAmjVrhpubG2fPniUyMpLChQvTpEkTRowYofbIaF5p0KAB+/fvZ82aNcpAeEZGRlSvXp0ePXpgZ2enkYDKS+PHj6devXqsW7eO4OBgkpOTKVeuHB06dGDgwIEULFgw3XmbNWvG4cOH2bJlC76+voSGhiq9vKtUqULbtm3p3bu3UpojN9WoUYP9+/fj7u7OwYMHuX79OnFxcZiYmFCxYkU6d+5Mp06dlEfQc4OOjg4TJkzA2tqadevWcenSJR4+fIiJiQktW7Zk5MiRREdHZ7iMYsWKsW7dOnx8fNi9ezeXLl1SRn83NzencePG9OvXT+2R/rRKlSqFm5sbixYt4urVqyQkJFCyZEkiIyMpX748kJoYW7BgAb1798bDw4Pz588THR1NcnIyJUuW5Msvv6RXr140atQo3TirVq3Kv//+y/bt2/H29ubGjRs8f/6cIkWKUK9ePb755hvatGmjMV/+/Plxc3Nj/vz5XLhwgRcvXmBiYkJUVJSSaNHV1WXKlCl069aNLVu2cObMGaKionj9+jXFixenQYMGODg4YGVllW58pqambN++nV27duHp6cmtW7dITEykQoUKdO/enX79+mVYruBDMTY2ZvPmzWzYsIH9+/dz69YtEhISKFiwIGXLlqVx48b07duXsmXLEhQUxO7du7l8+TLh4eFq5VJy4rxTpUoV9uzZw/bt29m3bx93797lzZs3VK1alT59+tC1a1eGDRuW7vzFixdnx44dbNmyhX379hEaGkpKSgrly5fH1taWgQMHqtVkza4yZcrg6enJ3r17OXDgAFevXuXp06fo6elRtmxZLC0t6devX6YJu/fRr18/EhMTcXd3Jzw8HAMDg2z3Fp8zZw4mJiYcPnyYJ0+eULhwYY0erZnR09Pjr7/+wt3dnd27d3P9+nVevnyJkZERpUuXplGjRvTu3ZsaNWqwePFiLl++zMOHDzl//nyGx3V2FSpUiA0bNrB3717+/fdfgoODefz4Mbq6upQoUYKGDRvStWtXtdrrKk2bNmXnzp2sWbOG06dPK6VmihUrRtWqVbGxscHOzo6kpCT+/PNP4uPj2b9/v0ZJm9w8F2WkdOnS7Nixg71797J//36uXr1KTEwMRYsWpVy5ctjY2NC1a9cc7+Gb1pAhQ2jevDlbtmzh/PnzREREkJCQQNGiRalcuTJt27blm2++ydXrnhBCCCGE0E4nJb0RZoQQQgghhBBCCCGEEEJ8cmQgNyGEEEIIIYQQQgghhPiMSNJXCCGEEEIIIYQQQgghPiOS9BVCCCGEEEIIIYQQQojPiCR9hRBCCCGEEEIIIYQQ4jOil9cBfGjJycnvNXJ3btHV1f0o4xKfD9nHPn2hoaHo6OhQqVKlvA5FfKbkPPHpCw0NBaBy5cp5HIkm2b9EbpL9K3sMDAzyOgQhhBBC5DKdlJSUlLwO4kNKTEzk8ePHeR2GBhMTk48yLvH5kH3s0+fg4IC+vj5bt27N61DEZ0rOE58+BwcHADw8PPI4Ek2yf4ncJPtX9pibm+d1CEIIIYTIZVLeQQghhBBCCCGEEEIIIT4jkvQVQgghhBBCCCGEEEKIz4gkfYUQQgghhBBCCCGEEOIzIklfIYQQQgghhBBCCCGE+IxI0lcIIYQQQgghhBBCCCE+I5L0FUIIIYQQQgghhBBCiM+IJH2FEEIIIYQQQgghhBDiMyJJXyGEEEIIIYQQQgghhPiM6OV1AEIIIYQQQgghxPtISEjg8ePHxMbGkpycnNfhCCGEELlGV1eXwoULY2JigqGhYbrtJOkrhBBCCCGEEOKTlZCQwO3btzE0NKRo0aLo6uqio6OT12EJIYQQOS4lJYXk5GTi4+O5ffs2FStWTDfxK+UdhBBCCCGEEEJ8sh4/foyhoSGFChVCT09PEr5CCCE+Wzo6Oujp6VGoUCEMDQ15/Phxum0l6SuEEEIIIYQQ4pMVGxuLkZFRXochhBBCfFBGRkbExsam+7okfYUQQgghhBBCfLKSk5PR1dXN6zCEEEKID0pXVzfDOvaS9BVCCCGEEEII8UmTkg5CCCH+azK79knSVwghhBBCCCGEEEIIIT4jkvQVQgghhBBCCCGEEEKIz4gkfYUQQgghhBBCCCGEEOIzIklfIYQQQgghhBDiMxAXF0fz5s3ZvXu3Mm3ZsmXY2NiQkpKS4bzx8fHMmTMHW1tbWrduzYQJE3I73GyJi4vD0tISJyend5p/3759WFpasm3bthyOTLt79+7h4+OT423/ayZPnoylpSWRkZF5HUq2vHr1is2bNzNs2DBsbGxo27Ytffv2Zd68eYSGhmZ7eZaWlgwcOPCdYvmQ+76TkxOWlpbExcXl+rpE5iTpK4QQQgghhBBCfAaCgoJISUmhVq1aatNq1aqV6YA/a9euZd++fZQuXZpvvvmGVq1a5XK0n6+bN2/y7bffcvny5RxtKz4NISEh9O3blz///JPY2Fjat29P9+7dKVOmDPv27WPgwIGsWbMm0xsxaTk6OmJvb/9O8VStWhVHR0dq1679TvOLT5deXgcghBBCCCGEEEKI9xccHIyRkREVK1YEIDk5mRs3btC7d+9M571x4wYAP//8M2XKlMnVOD93cXFxvH79Osfbio9fVFQUzs7OvHr1ikmTJtGlSxe1Gy4RERFMmTKF1atXk5SUxPfff5+l5X733XfvHFO1atWoVq3aO88vPl3S01cIIYQQQgghhPgMBAcHU61aNXR1dQG4c+cO8fHxaj1/06NKPBYpUiRXYxTic7Zo0SJiY2MZO3YsdnZ2Gj3sS5cuzdKlSylatCgbN24kJCQkjyIV/wWS9BVCCCGEEEIIIT5RqhqalpaWnDhxgkuXLil/f/vttwBMmDABBwcHrfNfuHABS0tLLl68CICNjY1SQ3X16tVYWlpy7tw5hg4dSqtWrejduzcvX74E4PLly0yZMgU7OzusrKywsbFhzJgxnD9/XmuMb9f5jIyMxNLSksmTJ2tM//nnn+ncuTPt2rVjypQpPHjwQCN2VXzHjh3TeM3BwYEOHTpkuv0eP36Mi4sL9vb2tGrVih49euDq6sqLFy/U2s2aNQtLS0tiY2OZP38+Xbp0oXXr1jg6OnLkyBG1mJydnQFwd3fH0tKSCxcuaF13em3nzp2LpaUlS5YsUWt/6dIlWrRowYABA0hMTAQgLCyMGTNm0L17d1q1aoWDgwMuLi48fvw40/eu2n6hoaEsXryYTp06YW1tzZgxY7SWm0hJSWHnzp0MHjyY1q1bY2Njw8SJE5Ve4iqqGrI+Pj6MGzeO1q1b0717d8LDw9ONJTk5mU2bNtG7d29at27Nt99+i6+vb7rt9+/fj5OTEzY2NlhZWWFnZ8fMmTPV1jF69GgsLS0JCwvTOr+lpaVa/ev3FRERwcmTJzE3N6dLly7ptitcuDADBgwjDIBpAAAgAElEQVQgOTlZbf1OTk44ODhw+vRpHBwcaNOmDTNmzAC01/R98uQJv//+O127dqVNmzaMGDGCK1euMGbMGLXjXVtNXwcHB5ycnLhz5w4TJ07E2tqadu3a8cMPP3Dz5k2NmLN6rIuPy0eT9E1MTKRz586cOnVK62udOnVi6dKlatP9/Pzo0qUL9evXZ8CAAdy9e/dDhSuEEEIIIYQQQuQ5W1tbHB0d6dGjBwDt2rXD0dERR0dHqlWrRuHChXF0dOSbb77ROr+5uTmOjo6YmZkB0L9/fxwdHTE2Nlba/PzzzxgaGtKjRw8aNmxIgQIFOH78OE5OTgQGBtKyZUt69epFnTp1OH/+POPGjdNIBGbVw4cPGTZsGAcPHqROnTp07tyZ27dvM378+HdaXkaioqIYMmQI//zzD9WrV6dXr16UK1eOTZs24eTkRHx8vMY8Y8eOxc/PjzZt2mBtbc3t27eZMWMGAQEBAFhYWGBrawtArVq1cHR0xNzcXOv602s7evRoSpUqxY4dO5TtGB8fz+zZs9HV1WXGjBkYGBjw9OlTRo8ezalTp7CwsKBXr15UrFiRnTt34uzsTFJSUpa2w+zZszlw4ABt2rShRYsWXLlyhdGjR+Pv76/WbtasWbi4uPD69Wvs7e1p3bo1ly5dYtiwYVqTf4sWLSImJoYePXpQs2ZNvvjiiwxjcHV1RVdXl65du1KyZEmmT59OYGCgRttly5Yxa9Ys4uLisLW1xcHBgRIlSnDw4EGcnZ1JSEgAUm9gAHh7e2ssw8vLCwMDA9q0aZOlbZQV586dIyUlha+//pp8+TJOt1lZWQFw/PhxtenPnj1jxowZ1KtXj44dO1KvXj2t8z979owRI0awa9cuKlWqhIODAwkJCYwePZo7d+5kKd4HDx4wfPhwnj59ip2dHQ0bNsTPzw9nZ2e1mx65dayL3PdR1PRNSEhI924CwF9//aXR5T0yMpIRI0YwcuRIWrduzZ9//snIkSPZs2dPpgeXEEIIIYQQQojPn2HgNvJf2ZzXYWToVd2+JNTu9c7zd+rUCYATJ06wY8cOHB0dKV++PABnzpyhbt26GdYDNTc357vvvuPixYtERUXRv39/ChUqpNbG1NSUJUuWqP3WdnV1xdjYmLVr11K8eHFl+qZNm3B1dcXHx+ed6oiuWLGCx48fM23aNDp27AikJjwnTJiQpd6r2eHi4kJ0dDTz5s3D0tJSme7u7s7ixYtZs2YNo0aNUptHV1eXjRs3YmRkBECjRo34+eef2bNnDw0aNKBhw4ZAak/S2rVrZ7jtM2o7adIkfvjhB+bPn8/KlSv566+/CA8PZ/jw4cp2PXz4MA8ePGDq1KnKfgCwYMECPD09OXPmDM2aNct0O4SFhbFmzRqllnP37t0ZMWIELi4ubN26FV1dXXx8fDhw4ADt27dn+vTp6OmlppMGDBjAd999x6+//oq7uzv6+vrKcvX09Pjrr7/Inz9/hus/f/48Xl5eNG7cmN9++w0DAwMAPDw8WLhwoVrb6Ohotm3bRoMGDViyZIlSygTgxx9/5PTp0wQEBNC4cWNatWrFwoULOXToEI6Ojkq7x48fc+HCBaysrNRubryve/fuAVCuXLlM25qbm2NoaEh0dDSvX79Wtlt8fDy9e/dWeoCnZ/Xq1YSFhTFq1Cj69u0LwJs3b/jf//6Hj4+PchMnIxEREXTv3p3x48crZSjmzZvH7t27OXLkCJ07dwZy71gXuS/Ps6MhISF88803ysHxtmvXruHu7k6lSpXUpm/fvp0aNWowdOhQqlSpwpw5c4iMjMTPz+9DhC2EEEIIIYQQQnw0rl27RsGCBZWEU1JSEjdv3sxSPd/MtGzZUi3h++bNG4YPH8706dPVkkCQ2nsV4OnTp9lez+vXrzl69CgVK1ZUEr4ARkZGjBgx4h2j1+7Ro0f4+fnRtGlTtYQvpD76bmpqyr///qsxn4ODg5LwBWjatClAujmNd9WkSRM6duxIUFAQ8+fPx9PTk7p16yoJPkgttwAQGBhIcnKyMn3YsGHs3r07SwlfgB49eqgN3le7dm3atWtHRESE0tN27969AIwZM0ZJ+EJqjdpu3boRHR3N2bNn1ZbbtGnTTBO+AIcOHQJg6NChSsIXUre16gaGioGBATNmzGDMmDFqCV/4/30vJiYGgAIFCmBlZcXdu3fVeqMePHiQ5OTkLJX/yA5V79gCBQpkqX2hQoVISUnh2bNnatNbt26d4XzJycl4e3tjbm5Or17/f8MoX758jBo1SmO7ZKR///5qdYdV+/P9+/eB3DvWxYeR5z19z507h6WlJc7OzjRo0EDtteTkZKZOncqPP/6oVnsEUmvZfPXVV8rfRkZG1K5dm4sXL2b5xCaEEEIIIYQQ4vOVULvXe/Wi/ZQEBwdTvXp1JYFz69YtEhISqFmz5nsv++3yBPny5VMeT4+KiuLWrVuEh4dz+/ZtpX7tmzdvsr2e8PBw4uPjqVGjhsZrNWrUUEs2vq8bN24oCbfVq1drvK6np8eDBw+Ijo6mZMmSyvSyZcuqtVP1FFUNhJeTRo8ezdmzZ9m1axdGRkZMnz5dLaHXqlUr3Nzc2LVrF0ePHuXrr7+mSZMmNG3aFBMTkyyv5+1cDEDNmjXx8vIiJCSEevXqcf36dQwMDPD09NRoqyq1efPmTbV8TFZ6m0JqZ0BdXV2qVq2q8VrdunXVSnkWKVIEa2tr3rx5w61bt7hz5w4RERGEhIQoSee0CXAbGxsOHDiAt7e30hvV29ubokWL0qRJkyzFl1WqHvKq8hKZefXqFTo6OhqDJ2a23cLCwoiLi6NRo0YaCV4zMzNKlSql3BDIiIGBAaampmrTChYsCKDUjM6tY118GHme9O3du3e6r61evZpixYphb2+vkfSNjo6mVKlSatNMTEy0FncXQgghhBBCCCE+R05OTsogbIBGr1VVLVwzMzM8PDzeaR2GhoYa00JDQ1m0aJGybj09PSpUqECNGjW4f/9+lpJOb1MN9Katp6Surm6OPoqvWldgYKDWurEqsbGxaknftOULACXJ/i7vNzOFChWiUaNGHDhwAFNTU41kYMmSJVm1ahXr1q3j2LFjeHt74+3tjb6+Ph07dmTMmDFaP7u3pX1/Kqqk8fPnz4HU7ZWcnMyaNWvSXU5sbKza31lZt2rZBgYGWpP6b5caAfD19WX58uVKb1QjIyNq1KhBlSpVlLq6Ko0aNaJUqVIcPnyYUaNGce/ePa5fv46Dg0OGNxHi4uLYvn27xvSOHTumW6NZVbNYFVdGHj16xPPnzylRooTGPpXZdlP1DH67561KiRIliI6OzjSGtL2qVbTtz7lxrIsPI8+Tvum5ffs2q1evTveiFB8fr7GDGhgYKHcj0qOrq5utO14fip6e3kcZl/h8yD726dPX10dHR0c+R5Fr5Dzx6VP9aPgYP0fZv0Rukv1L/JfZ2tpSsWJFPD09adeunVLe4ejRozx58oRu3boB5GjC9MWLF4wdO5bnz5/j5OTEV199Rfny5dHX1ycwMJCDBw9qne/t5NCrV6/U/lYl+NIOIpV23rcHVsso4fr2st+mSiwPGjSIoUOHZtg2r1y4cAEvLy8KFy7MnTt3WLdunUaN4NKlSzNlyhQmTpzItWvX8Pf3Z9++fezatQtjY2NGjhyZ6Xq09UxVJXuLFi0KpG6vAgUKaO3p+74KFSpEeHg4SUlJGonYtz/zwMBAZsyYQcmSJfn555+pUaMGX3zxBTo6OmzcuJFz586ptc+XLx/W1tZs3LiRq1evKr2BVQPopef58+daE9wWFhbpJn2bN2/OggULOHnypNbyE2mpBnBr2bJlhnFoo+qN+/LlS62vazt+3tW7Huvi4/BRJn1TUlKYNm0aI0aMUKsrk5ahoaFGgjcxMVE5IaUnOTk5x4u/5wQTE5OPMi7x+ZB97NOnKvAvn6PILXKe+PSpHi39GD9H2b9EbpL9K3vSS1iIT1OnTp0oUqQInp6eDBw4UBkPx9/fP9NB3N7VhQsXePLkCX369KFPnz5qr925cwdQT8SmHaSqcOHCyvTw8HC1ecuUKYOxsTFXrlzRWOft27c1kpOqBOHbicG4uDhiY2MzrK1auXJlILUWsjarVq3C0NCQ3r17a/TEzEzaGqnv2jY+Pp65c+diYGDA8uXLmTp1KuvXr6dly5ZKGYTjx4/j7+/PiBEjKFiwILVr16Z27dp06tSJ7t27c+nSpSzFEBwcrFFS4+rVqwBKTejKlStz6dIlHj9+rHGT7eTJkwQGBtK6dWutJRoyU716dYKCgrh69apGqYm3P59Dhw7x5s0bfvzxR43Snqp97222trZs3LiREydOcP78ecqVK5dp2RNzc3NOnjyZrfdhYmJCq1atOHz4MDt27FCrt5vW8+fP2bBhA/ny5aNLly7ZWgdA+fLlMTIyIigoSOO1uLg47t27R4kSJbK9XG2ye6yLj0ueD+SmTUREBOfPn2fx4sVYWFhgYWFBQEAAK1asYMiQIUDq6KFvd1d/9OiR1scShBBCCCGEEEKIz9XNmzcxMDBQBr1S1Tt9lwRcVqieun17AKeoqCjc3NwA9bqqqrhOnTqlTEtISGDz5s1q8+vp6dG+fXvCw8PZunWrMv3169csX75cIw7Vct9Ozq1fvz7TOqOlS5emQYMG+Pn5ceTIEbXXDhw4gJubG/7+/tlO+KreB6QOpveubV1dXYmIiGDw4MGUL1+eCRMmkJyczJw5c5S29+7dY+fOnfzzzz9q80ZGRgJZr6m7efNmHj16pPx95coVvL29qV69OlWqVAFSyxqkpKSwcOFCtfrFjx49wsXFhQ0bNqgNcJcdtra26OjosHz5crVeqocOHdJI+qr2vSdPnqhNP3funNLr9O1tqSpFcPDgQa5du4aNjc07xZkV48aNo2jRovz555/s3LlTIyEaFRXFuHHjePDgAf369VPqDGeHnp4e1tbWyuev8ubNG1xdXbO032VVdo918XH5KHv6mpqa4u3trTZt3LhxNGzYUHnson79+mrd9uPj4wkKCsrxET2FEEIIIYQQQoiP2c2bN6lUqZLyOLlqQLTcSvrWq1cPc3NzDhw4QExMDFWqVOHhw4ccP34cQ0NDdHR0lLqjkNob2dPTk8WLF3P16lWKFi3K8ePHMTY21kgUfv/995w7d46lS5fi7+9PhQoVOHfuHLGxsRolHps1a0aJEiXw8fHh+fPnVK1alStXrnD79m0qV66c6Zg/EydOZOTIkUyfPp0mTZpQqVIl7t27x6lTpyhcuDA//PDDO20fVWc0Hx8fjIyMsLW1VXpgZ6VtbGwsO3fupFKlSkrvygYNGtCpUyf27t3Lhg0bGDx4MF26dGHXrl24urpy4cIFqlSpwtOnT5VlDRgwIEvxvnjxgsGDB2NlZcWLFy/w9fXF0NCQSZMmKW06duzIiRMn8PX1ZcCAATRu3Jjk5GR8fHx49uxZhk9qZ6Z27dr06dOHzZs3M2jQICwtLXnw4AHHjx/niy++UOsR3rZtW7Zu3cqCBQu4ePEiJUqUICQkhDNnzlCkSBGePn2qtu+p2NrasmjRInR0dLC2tn6nOLOiWLFiLF++nIkTJ+Li4sKOHTto1KgRRkZG3Lt3D39/fxITExkwYMB7lRUZOnQofn5+uLi4cOLECSpUqEBAQAB3797F0NCQfPlypo9ndo918XH5KHv66unpUb58ebX/DA0NKVKkiDKyoIODA5cuXeKvv/4iJCSEadOmUbp0aZo2bZrH0QshhBBCCCGEEB9OSEiIWoI3JCQEINeSvkZGRixevBgrKyuuX7+Oh4cHN27coEOHDqxbt44qVapw6dIlpeZo1apVmT9/PjVq1MDHxwcvLy++/PJL/vjjD426p4ULF2b58uXY29sTGhrKrl27KF68OEuWLNFI+urr67Ns2TJatmxJYGAgO3fupGDBgvz111+ULl060/dRvnx51qxZg52dHaGhobi7uxMSEkKHDh1YtWoVFStWfKftY2Zmxvfff4+Ojg4eHh4EBwdnue3FixeZM2cOAJMmTVKrcTtq1CiKFi3KunXrCA0NpXDhwvz5559069aN+/fvs337dk6ePEnTpk1ZuXKl0ks3M+PHj6dly5YcOnQIPz8/mjVrxooVK6hevbrSRkdHh1mzZjF27Fjy58/Pnj17OHz4MBUqVGDu3Ln079//nbZV2vc2efJkjIyM2LVrF7du3WLy5MkaJRyqVauGi4sL1atX5/jx4+zatYsnT54wZMgQ1q1bR758+Th9+rTG8tu2bQukdiDM7RI3ZcuWZe3atUyaNIkiRYpw5MgR3N3duX37NjY2NqxevZrhw4dnWPM3M6rksrW1NcHBwXh6emJkZMTSpUspUKAA+fPnz5H3kt1jXXxcdFI+ouIb1atXx83NTeOgBujTpw/NmjXD2dlZmXb06FHmzp1LZGQk9evXZ9asWUrR+vQkJiZ+lPW+pA6ZyG2yj336HBwc0NfXV3vUTYicJOeJT5+DgwPAO4/Onptk/xK5Sfav7PncavoGBgYqnYOEEFm3evVq1qxZw9y5c99pQLFPib+/P+PHj2fy5MnvVEf3YxMWFkapUqU0boQkJibSvn17vvzySxYsWJBH0YkP6cGDB9SuXVvrax9VeYfr16+n+9qWLVs0pllZWWFlZZWbIQkhhBBCCCGEEEKIT9Tr16/ZsGEDBQsWVHr8fuomT57Mo0ePcHd3p1ChQsp0d3d3kpKSaNiwYR5GJz4WH1XSVwghhBBCCCGEEEKI9xUWFsa0adN49OgRMTExDBkyhAIFCuR1WDmiW7duLFy4kG+//ZbmzZtjZGTE9evXOXfuHFWqVKFHjx55HaL4CEjSVwghhBBCCCGEEEJ8VooXL05cXByJiYk4ODhkeWC7T4GDgwMmJibs2LEDHx8fXr58iampKQMGDODbb7/F0NAwr0MUHwFJ+gohhBBCCCGEEEL8x3z33Xd89913eR1GrilQoACenp55HUauadWqFa1atcrrMMRHLF9eByCEEEIIIYQQQgghhBAi50jSVwghhBBCCCGEEEIIIT4jkvQVQgghhBBCCCGEEEKIz4gkfYUQQgghhBBCCCGEEOIzIklfIYQQQgghhBBCCCGE+Izo5XUAQrwrNzc31q1bl6W2pqambNu2LUfWu3//fubNm8eoUaPo2bNntucfM2YMly5dYs+ePRQqVChHYvrYzZ07Fy8vL/7++2+qVq2aY8tdunQpHh4eLFq0CAsLixxbrvi8aDtX6OjoYGhoiImJCRYWFvTs2ZPy5cu/8zoiIyPp06cPlpaWzJ49+31D/iS0atWKypUrs3r16hxdbqdOnTA2Ns6xc7YQmcmr7xNp3b9/n9DQ0I9yBG65hgshhBBCfJok6Ss+WQ0aNNCYduDAAR48eICDgwPGxsbK9LT/fl9VqlRh4MCB1KpV653mt7GxoUGDBhgYGORYTB+75s2bY2ZmRvHixfM6FPEfZmlpSZUqVQBISUnhxYsXhIaGsnfvXg4ePMjMmTNp2rRpHkf56Rg4cKAc0+KzkFffJ1RCQkIYMWIEdnZ2H2XSV67hQgghhBCfJkn6ik+WhYWFRs+QgIAAHjx4QI8ePTA3N8+V9VatWvW9errY2trmYDSfhhYtWtCiRYu8DkP8xzVv3lzr8efn58eMGTP4+eefWbVqFWXKlMmD6D49gwcPzusQhMgRefV9QiUuLo7Xr1/n6jreh1zDhRBCCCE+TVLTVwghxH9akyZNcHR05NWrV2zYsCGvwxFCCCGEEEIIId6b9PQV/xkXL15k3LhxjBs3jkuXLnHixAmMjY35+eefqVu3LlFRUWzevJlz584RHR2Nrq4u5cqVo1OnTnTt2lVZjraavr169cLMzIzx48ezfPlyLl++zJs3b6hXrx5Dhw5VHikHzZq+qrgmTZpESkoK7u7uhIWFUaRIEdq0aYOjoyP58+dXey+7d+9m586dREREULx4cezs7ChevDhz587NtDaeqnbhmjVr2LdvH4cOHSIpKYnq1aszePBg6tatq9Y+JSWF3bt3s3fvXu7evYuBgQH16tVj8ODBaj2eVdtl5syZ7Nu3j4CAAIoXL86iRYtYt26d1nqAPj4+eHp6EhISAkDlypXp3r07bdu21Yj733//xcPDg7CwMEqUKEGvXr2y8rELkSXdunXDzc2No0ePMmHCBPT0/v/yeOHCBTZt2sS1a9dITk6mUqVKfPPNN1l6DDsr55XLly8zevRo2rVrx/Tp0zWW0bdvX5KSkti6dSv58mm/V9urVy9KlizJuHHjWLJkCdeuXaNQoUI0b94cR0dHChcurNb+8ePHrF+/npMnT/Ls2TNMTExo3bo1Y8aMUWs3ZswYoqKiGD9+PAsXLiQmJoamTZsyc+ZMrTV9nz9/zsaNGzl27BgPHz6kUKFCNGrUiIEDB1K2bFm1ZcfExLB27VpOnDhBXFwctWvXxsnJKdNtKkRee/HiBRs3buTo0aNER0dTpEgRmjVrxuDBgylWrJhaW09PTw4cOMD9+/fR0dGhcuXKODg4KOePtPWEPTw8Mq1xK9dwIURm4uLisLW1ZeLEidjZ2QGwbNky9u7dy/79+9HR0cnjCP8bVq9ezZo1a5g7dy4tW7b84Ovftm0bS5YsYerUqXTq1OmDrz+74uLisLGxwcLCgmXLluV1OB/Ep/ae9+3bx5w5c7S+ZmBgQOHChalVqxb9+vWjTp06Hzi6dxMZGUmPHj1o0aIFv/32W7bnz+vjPCsk6Sv+c9auXYuRkRHdu3fnzp07VK1alcjISIYPH86rV69o0aIFpUqV4tGjRxw9epRFixaRnJxM9+7dM1xudHQ0Tk5OlClThs6dO3Pv3j1Onz5NUFAQ27Zto0CBAhnOv3PnTm7dukXLli1p3Lgxx44dY/v27Tx//pyJEycq7ZYtW8aOHTsoXbo0nTt35tmzZ6xatQpTU9NsbYfffvuNiIgI2rVrR3x8PL6+vowfP545c+bw1VdfKe3mzp2Lt7c3FSpUwM7OjlevXnHkyBFGjRrFb7/9RsOGDdWW+8cff1CiRAkcHByIiIigdOnSWtfv6urK9u3bKV68uPID8fTp0/z666+EhIQwbNgwpe3q1avZsGEDpqamdOzYkSdPnrB48WKKFi2arfcsRHry589PtWrVuHr1KiEhIdSoUQOAvXv3smDBAooWLUrr1q0xMjLi5MmTzJw5kyFDhtC/f/90l5nV80rdunUpXbo0J0+e5NWrV2o3ea5evUpERAR9+/ZNN+Gr8vjxY8aOHYuZmRndunUjKCiIf/75h4CAAP766y+MjIwAePDgAU5OTjx69IhmzZpRrlw5QkJC2LJlCwEBASxcuFBpCxAbG8vMmTOxtLSkYMGC6Q549+zZM5ycnLh//z61atXC0tKSiIgIfHx8OH36NPPnz1dqob98+ZIxY8Zw9+5dGjZsSKVKlQgICGDMmDEkJiZm7UMTIg88f/4cZ2dnbt++TcOGDWnZsiURERHs3bsXf39/XF1dMTExAWDz5s2sXLmSatWq0aVLF5KSkvD19WXmzJlMnToVa2trGjRoQFRUFF5eXtSsWZOvv/4aMzOzTOOQa7gQIj1BQUGkpKSojT8SFBRErVq1JOH7AVlYWODo6PheAwUL8THSVhYrLi6OoKAgjh07xsmTJ1m6dCn169fPowizztjY+L2O00/hOJekr/jPiY+PZ9WqVcqPMkj9Yfbs2TNcXFz48ssvlendunVjxIgRHD58ONOkb0REBPb29owZM0b5QuXi4sLevXvx9fWlY8eOGc4fEhLC0qVLqV27NgD9+/enf//+HDp0CGdnZ4yMjLh27RoeHh7UqlULFxcXJZHctm1bpkyZkq3tEB4ezsqVK/niiy8A6Nq1K87OzixatIgNGzagq6uLr68v3t7etGvXjsmTJyu9H/v168ewYcOYM2cOW7ZsQV9fX1munp4eS5cu1eidnNbly5fZvn07VatWZf78+coPv5iYGMaNG8eWLVto0qQJ9evXJywsjM2bN1OlShUWLVpEoUKFADh16hTTpk3L1nsWIiMlSpQA4MmTJwA8fPiQP/74g3LlyrFkyRKKFCkCwJAhQ/jhhx9Ys2YNzZo1o1KlSlqXl9Xzio6ODu3bt2fdunWcOnWKNm3aKG0PHToEgLW1dabxR0ZGYmlpyS+//IKuri4AS5YswdPTk61btyo1eBctWsSjR4+YPXs2zZo1U+b38PBg6dKlrFu3juHDhyvT4+Pj+eabbxg5cmSG61+xYgX3799nwIABfPfdd8p0Pz8/pkyZwpw5c1i3bh26urps27aNu3fvMmjQIAYNGgRAUlISc+fO5fDhw5m+VyHyyqpVq7h9+zZjx47F3t5emX7y5EmmTZvG0qVLmTlzJgBbt26ldOnSuLq6KtfP3r17069fPzw8PLC2tlZ+NHl5eVGrVq0s18qWa7gQIj3BwcEYGRlRsWJFAJKTk7lx4wa9e/fO48j+Wxo2bKhxY02Iz4GFhYXad/20/v77b9auXYurqysrVqz4wJFlX6FChdJ9L1nxKRznUtNX/OfUrVtXLeEL0L59eyZOnKiWmAGoWbMmhoaGPH36NEvL7tu3r9od9MaNGwNw//79TOdt0KCBkvCF1LtOtWvXJjExkYcPHwLg7e1NSkoKQ4YMUes53LRpU43YM9OtWzflxyJArVq1aNOmDREREQQFBQGpj3AAODk5qT3ubm5ujp2dHY8ePeLcuXNqy23SpEmGPxYhdVR0gBEjRqj19ClatCjff/89kPooKICvry/Jycn0799f+bEI0KxZM7XeTEK8L1Xi48WLFwAcPHiQ169fM3jwYCXhC2BoaMigQYN48+YNXl5e6S4vO+cVVVI3bcIzKSmJI0eOUK1aNSpUqJBp/Do6OgwfPlxJ+AI4OjpiZGSkJI8fP36Mv78/jRs3Vkv4QjMHm3EAACAASURBVOo5wczMjP3792ss28rKKsN1v379msOHD2NqaqqRtGrSpAktW7YkLCyMy5cvK+/T2NhYrae0np4eI0eOlF5I4qOVlJSEl5cXFSpUUEv4AlhaWlKnTh2OHTumnENSUlJ49uyZ2neAUqVKsX79epYsWfJescg1XAiRnuDgYKpVq6Z8H7hz5w7x8fFqPX+FECI3DBo0CD09Pa5evcqrV6/yOhyB9PQV/0HaHpusV68e9erVIzY2lpCQEMLDw7l//z5BQUEkJiby5s2bTJdrYGBAqVKl1KYZGxsDZGlU7jJlymhMe3v+a9euASiPnqdVp04djR9vGWnQoIHGtJo1a3Lw4EFCQ0OpW7cuN27cwMDAgJ07d2q0vXfvHpDaQ7lp06bK9Kw8lhoSEkK+fPk0ag8CyrTQ0FC1/1evXl2jbZ06dThz5kym6xMiK16+fAmglDa4ceMGkFrT9/bt22pt4+PjAZRaltpk57zyxRdfKPtzXFwchQoV4syZMzx79izDEhJpmZiYaNTNNTY2pmzZsty4cYNXr15x48YNUlJSiI2Nxc3NTWMZ+vr6REVFER0dTcmSJZXpmR3X9+/fJyEhgbp162otQ1G3bl2OHj1KaGgotWrVIiwsjPr166slolTvwdzcnOTk5Cy9ZyE+pPv37xMfH8+bN2+0Hj+q4/rWrVvUrVuXLl26sHnzZr777juqV69O48aNadKkidZreHbJNVwIkZaTkxMXL15Um2Zpaan294QJEzAzM8PDwyPDZQUEBLBp0yYCAwNJSkqiYsWK9O/fnxYtWqi1O3PmDJs2bSIoKIikpCQqVKhAly5dsLe3V/su4ODgQJkyZRg9ejR//vknly9fxsDAgFatWjF69Gji4uJYsmQJ/v7+6Ovr8/XXXzNmzBjlppKq5ubAgQOpUqUKq1evJiIiAlNTU7p27UqvXr2U9anaDho0iOfPn7N3714MDQ358ccfadOmDYmJiWzZsgVvb2/Cw8MxMjKiXr16DBo0iJo1ayoxX7hwAWdnZ3766ScSEhLYunUrkZGRmJmZMWjQIDp06MDx48dxc3Pjzp07lCpVip49e+Lg4KAsQ1utz7CwMFasWEFgYCBPnjzBxMSEpk2bMnjwYI1OSdevX8fNzY1Lly7x6tUrypUrh729Pfb29ho3yI8fP86GDRsIDQ2lcOHC2NvbY2BgkOHn/LZ9+/bxzz//cPv2bYyMjKhduzZDhgxRG5vm5cuXbNu2DV9fX8LDw0lOTqZUqVK0bNlS6WiQdvv9+OOPBAQEcOzYMQoVKsSsWbOoV68ekZGRrFy5krNnz/Lq1Su++uqrdHtbHjx4EA8PD+7cuUNSUhLly5enc+fOWrfD2xwcHChVqhQ//PADixcvJigoiMKFC9OiRQuGDh2qdcwLNzc3Tpw4QUxMDCVKlKBNmzYMHDiQggULKu2cnJyIjIzkxx9/xMXFhadPn2Jpacmvv/6abizZec8xMTFs3LiRU6dOERUVBaTesLW2tqZfv37o6ekRFRVFjx49qFu3Ln/99ZfGMpydnQkMDGTPnj1qsec2fX19jI2NiYmJITExUe1G8vnz51m/fj3BwcEkJydTuXJl+vTpQ+vWrdWWYWlpSefOnbGxsWHlypVcv34dY2NjOnbsyNChQ7l37x5Lly7l8uXLFCxYECsrK0aOHKm2rqzuq9pq+s6aNYv9+/ezf/9+VqxYwbFjx3j+/DkVK1ZkwIABavFqO84tLS2xtbWla9eurFixgmvXrqGnp8dXX33FyJEjMTc3V3u/58+fx83NjRs3bqCvr4+VlRU9evRgwIABODo6vldPZJCkr/gP0nYBjIuLY9myZRw+fJikpCR0dHQwMzPDwsJCSZJkJu3jkW/LqfmfPXtG/vz5tdYHVj2anlXa2hcvXhz4/56Oz58/Jzk5WRlkRpu4uDi1v7PyBePFixcYGBhofc/Gxsbkz5+fhIQEteVre89pew0J8b5UX6pUNSyfP38OpA6cmJ639/+3X8vOeaVDhw5cvXqVo0eP0rlzZw4ePIiurq7WQZG0Se8ckPa4Vr2noKAgpTdgerGnTfpmdlyrzhmqG1XpxZaQkJDhMQ2px3VMTEyG6xMiL6iOn3v37mXpujh06FDKlCnD7t27uXbtGsHBwaxdu5ayZcsybty493ocUK7hQoi0bG1tsbCwIDY2lh07dtCuXTvKlSsHwIkTJ5QEUXrXaRUvLy9mz56NoaEhLVq0oEiRIvj6+jJ58mSmTJlC586dAXB3d2fx4sUYGxvTsmVLChQogJ+fHwsWLCAgIICff/5ZLSEXERHB8OHDqV27Nvb29vj5+bFr1y5iY2MJDg5WBqa+cuUK3t7evHr1irlz56rF5ufnx/r162nWrBlff/01p0+fZtmyZYSGhmoMhKv67tatWzfu3r1L7dq1SUhIYOzYsVy+fJnKlStjb2/PkydPOH78OH5+fsyaNUsjsb1lyxbCwsJo164djRo14t9//+WXX37h5s2buLu707p1a+rXr4+3tzcLFy6kVKlSGstQefr0KaNHj+bZs2e0atWKEiVKEBoays6dO7lw4QLr169XboafPn2aqVOnoqenh5WVFcWKFcPf3x8XFxdu3LjBpEmTlOXu2bOH3377jWLFitGhQwdevXrFunXrMv2s0/r999/ZtWsXZmZm2NjY8Pr1aw4ePMi5c+dYvnw5VapUISkpiTFjxhAcHEzjxo1p3LgxL1++5MSJE2zevJmIiAhmz56tttw1a9ZgZGREjx49uH37NtWqVePhw4cMGzaMJ0+e0Lx5c8zMzPDz82P8+PEacR06dIiZM2dStmxZbG1tyZcvH8ePH8fFxYWYmJgslUR69OgRTk5OmJub4+DgQGBgIJ6enly8eJG///5bSf5FRUUxYsQIoqOjsbS0pEKFCty8eZNNmzZx9uxZXF1d1ca8ePbsGTNmzKBFixYZjnkBZOs9P3/+nKFDh/LgwQOaN29OixYtiImJ4ejRo6xcuZK4uDicnJwwMzOjQYMGBAQEEBUVpXbTNjo6moCAANq0afNBE76Q2kktJiYGU1NTtaT67t27+f333ylatCht27bFyMiI48ePM336dL7//nsGDhyotpzAwEC8vLxo2rQp3bp1w9fXlw0bNvD06VOOHj1K9erV6datG6dPn8bDwwNdXV1lQOp32Ve1GTt2LM+ePaNNmza8evUKb29vZsyYwbJly7TefE/rxo0bODs7U69ePWWsFR8fH27evMnmzZuVG1W+vr789NNPFChQgFatWmFoaMihQ4c4e/Zsdjd9uiTpKwQwe/Zs/Pz8sLOzw9ramkqVKik/UFSPRX8MChYsSGRkJElJSRo95P6PvTuPyynv/wf+ytXVXiRCSLYs2WKyRchQYXwpjG0mhZAswxjb3DNjxph7xowtW2QdamyZjDW0ijLUhCglkbIkW+vV+vuj33Wm09Vy5UbJ6/l43I97nOtc53zOOZ9zdV3v8/m83/IfecqS/yArSf6DVj6VXVNTE1paWjh48OBrtrhsWlpaSE5ORkZGhsIXEplMBplMJvyRkP8ozMzMFE2xB/4dbUn0v0pPT0diYiJ0dHSEL23yL3ZeXl7lFjOqSFU/VwYNGgR3d3cEBgZiyJAhuHTpEiwsLIRATmXKuqeBf+9rPT094Zg+//xzODs7K6xrYGCAtLQ0pfZXkvy4nj59Wubr8sCPnp6e6J4uC+9rqqnk98/QoUOxbNmyStdXUVHBsGHDMGzYMDx//hxXr15FSEgIgoODsWzZMvzxxx+vXcyMf8OJlJd44AASvbyquxkVMpk4ESaffvra7x8+fDiA4gDv4cOHRYWFLl++jM6dO1c6WuzVq1dYs2YNdHV1sWXLFiFo7OjoCEdHR2zevBm2trZ4/Pgx3N3d0ahRI7i7uwupZrKzs7F48WKcP38effv2ha2trbDtlJQUjB07FvPnzxe2OWrUKAQEBMDa2hrff/89VFRUUFBQgIkTJyI4OFihuG1sbCxmz56NiRMnAgBcXFwwb948nDp1CsOGDRM9SHv+/Dl27dqFtm3bCst27dqFa9euYdiwYVi8eLHwWyomJgaurq748ccfceTIEVGQLCEhAdu2bRNmaLRp0wa//PILvL29sXr1aiFVlpWVFdzc3ODn51du0Pf8+fN4/Pgxli1bJlwvAPjtt9/g4+ODy5cvo2/fvsjJycHKlSuhpaUFT09PYVTgrFmz8M033+DYsWPo378/+vbtKwwwMDQ0hIeHhzDrdOzYsZg9e3aF11vu6tWr8PX1RdeuXbF69Wrh+EeMGIFZs2Zh27Zt+OWXXxAYGIibN2/C0dFRSOUjb9f48eMREhKicM2ysrKwe/du0Sjm1atXIy0tDcuXLxdq3mRnZ2PRokUK30G9vLygqamJHTt2CO1ydnbGhAkTcPjwYUyZMqXS0b4pKSno168fVq1aJaQ8Wbt2LQ4fPizMxgGKa/Gkpqbi559/Fo2Slz/g2Llzp+icZmdnY/z48ZgzZ06l59jDw0PpYz569ChSUlKwePFijBw5Ulju7OyM8ePHw8/PD25ubgCKH/ZERkbi3LlzopmBfn5+KCwshI2NTaVtexOKioqQkZGB69evY926dUJ75Z48eYK1a9eiRYsW2Lx5s/B9YMaMGZg3bx48PT3Rv39/UZ2Uu3fvYu7cufj0/38ujhw5EhMnTsTx48cxYcIE4RxMmTIFo0ePxtmzZ4Wg7+v01bJIJBLs27dP+P7Xo0cPrFixAn/99VelQd87d+7A1dUVkyZNEs7RggULcPnyZURGRqJHjx7IycnBb7/9Bi0tLWzfvl2YsTlp0qQyf6e9Lub0pQ9eeno6wsLC0K5dOyxYsACdOnUSAhgPHz6sUZXkTU1NUVhYiNjYWIXXbt26VaVtyVNFlBQdHQ0AwvSm1q1bIzU1tcwg0MWLF+Hp6Vnh9PbyyKcJyfN7lnT9+nUUFRUJOUxNTU0BADdu3FBYt6zzQPQ6jh07hoKCAgwaNEj4Qti6dWsAZfezBw8eYPPmzbh48WKZ23udzxUdHR1YWloiKioKFy5cQE5ODoYMGaL0MTx48EAI+sjl5OQgPj4ebdu2hVQqrfCYAGDTpk3Yv3+/UilpSjI2NoaamhpiYmLKPLaoqCgAgImJCdTV1dGiRQvExcUpBK7S09ORnJxcpX0TvSvNmzeHVCotdwbQoUOHsHfvXrx8+RIvX77Erl27hPy3+vr6+Pjjj7FixQrY2dkhJycHcXFxAPBaeaz5N5yIyhITEwNtbW0hYJufn4+4uDil8vleunQJGRkZGDdunPB+oDhf99y5czFp0iRkZWXBz88PBQUFcHZ2FuUW19TUFIK6x48fV9h+yUJyurq6wufEp59+KnwOSiQSIR2MfAaWXOPGjTFu3DjR/uQBHT8/P9G6zZo1EwV8geJc4xoaGpg/f75o8Ez79u1hb2+P9PR0BAYGit7TpUsXUUoeeQobY2NjUW0E+fkt3eaS5H83oqOjRWmsZsyYgWPHjgnbk6cWmDRpkmgaeJ06dYRCu/K86fJrNnbsWFGawQ4dOsDOzq7ctpR09uxZAMUBsZIB786dO2PmzJlCANTU1BRLliwRgnBy2traMDU1RUFBAV69eiV6rUuXLqKAb15eHoKCgtCyZUtRkXNNTU3MmjVLoW1FRUWQyWRCyjX5/jw9PXH48GGl/n6qqKhg9uzZopoX06dPh6amptBvnj59irCwMPTp00chLYqDgwMaNWoknPOSSqclKEtVj7lXr15YtGiRQhH4Ro0awcjISDQbbuDAgdDQ0BCuoZyfnx/09fXRs2fPStv3Onbu3AlLS0vhf/369YOtrS0WLVqEly9fYs6cOcKsAKB4BkFubi6mTZumUCdl6tSpKCwsVDi/ampqsLe3F/7dokUL4UH5hAkThOXyUdbPnz8Xfle8Tl8ti4ODg2h0tzwdljxFVkXU1dVFn1cqKiro3bu36P1hYWF49uwZHBwcRCn6GjdurND2/wVH+tIHTyqVok6dOkhPT0deXp4wXVEmk2H9+vUAir8w1QR2dnY4fvw4duzYgVWrVglPpyIjIxESElKlbR04cAD9+vUT/hDfuHED586dg6mpqRAYsrW1RVRUFNavX4///Oc/wrlJS0vD2rVrkZaWpvQXipJsbW1x6tQpbN++HR07dhRV/t66dSuAfwtbDRo0CLt27cLevXvRo0cPob3Xrl1DaGholfdNVJp8Sp2mpqbwNBYoLsT2+++/w9PTU/SlNT8/H+vXr8fff/8tVMYu7XU/V4YOHYqAgABs27YNWlpa6Nevn9LHkZeXB09PT8ybNw8qKiooKirC9u3bkZOTI9ynTZo0QdeuXREeHo7AwEAMHDhQeL+fnx+2bt2Krl27is6DMqRSKQYPHoxTp05h165dmDFjhvBaeHg4AgIChLzFQPFngIeHB7Zt2wY3NzehvZ6ensznSzWWuro6rK2tcebMGRw8eFD0hTwyMhJbtmxBo0aNMHnyZBQUFODIkSPQ0NBA3759RVMc5UGBRo0aAYAQfKjKdw3+DSdSnsmnn/5Po2jfJ7du3UK7du2EYFhCQgJkMpkoX2155A+B5H+rSyqZakr+wKqskW6tWrWCrq6uwgMlVVVVhZzh8mBK6dlU8jQzpR8id+7cWWGmozzYWnp/pXNmZmZmIiUlBV26dClzunvXrl3h7e2tsJ3SNVfKa7O6unqZbS5p4MCB2LVrF3x9fREUFISePXuid+/e6NOnjygwKn+oFxsbix07dihsRyKRCNdA3t6ycsV37twZf/75Z7ntkYuPj4dEIimzj5QcPWpsbAxjY2PIZDJER0cjKSkJDx48QGxsrJBPunQdnNLXITk5GdnZ2WW2t3379grX9//+7/+wevVquLm5oU2bNsL56tKlS5k1JMpiYGAgeogBFA+0MDY2RmxsrKjmxcuXL8s856qqqnj8+HGVa168zjGbmprC1NQUWVlZuHHjBpKTk3H//n3ExMQgKSlJ9D1ZW1sbVlZW8PPzQ0JCAlq1aoWEhATEx8dj7NixCtt+U8zNzWFubg6g+N4KCAjAkydPYGNjg8WLFwv3g5z8Ie+VK1eQkJAgek0+60fep+UMDQ0V0khpaGhATU1NIf91yftPXV39tfpqWcqqlQIoV6+pcePGCu0v/X75oL2yHsp16dKl0n0oi0Ff+uBpaGigf//+CAoKwsyZM2FhYYHs7GxcvHgRz549g66uLjIyMlBYWKj0H5e3xczMDCNHjsSxY8cwbdo09OrVC8+ePUNwcDB0dHTw8uVL0VPMimRkZGDatGmwsrJCZmYmgoKCoKamhi+//FJYx9bWFqGhoQgODoazszMsLCxQUFCAgIAAvHr1Ci4uLqIn/Mrq2rUrxo0bh4MHD8LZ2Vl4sn3p0iWkpaVhwoQJ6Nq1K4DiD8xZs2Zh/fr1mD59OqysrJCRkYGgoCAYGhoiJSWlyvunD5M8p51cRkYG4uLicO3aNairq+Obb74RfXlr1qwZZs6cic2bN8PJyQmWlpbQ1dVFeHg47t27hz59+pQ7Evd1P1csLCygr6+Px48fw87OTuFLU0VUVVXh5+cnjOi5efMmoqOjYW5uLpoetnDhQsydOxffffcdevXqhZYtWyIpKQmXLl1C3bp18cUXX1TltApmzJiBGzduwNvbG1FRUejUqRNSUlJw8eJFaGpqYvny5cKP0DFjxiA0NBRHjhxBTEwMOnbsiOjoaNy9exf6+vqvtX+id2HWrFm4ceMGtmzZgtDQUHTo0AGpqakICQmBqqoqvvrqK9SpUwd16tSBs7MzNmzYACcnJ/Tv3x/q6uqIiopCTEwMhg4dKvwIlefnDQgIgKamJmxsbMp9oCTHv+FEVFLpQm6lRyvKc4dWVMhNnoqpshyg8vRM5a1nYGCgMGunomnUFdU1KalksE1OS0sLGhoaCjOdSn9/khfrLa/NJWsPlFReu5Vtc0kNGzaEp6cn9uzZg+DgYPj5+cHPzw9SqRTDhg3DvHnzoK6uLhxLRSkG5aMUK8qbXrpIWXnS09OhpqZWaYCwsLAQv//+O7y9vYX96uvro3PnzmjSpAkSExMVZsGUzhNfUXslEolCyqBRo0ZBX18fhw4dwrVr1xAfH499+/ahYcOGmDNnjlJ1L8rqN8C/efAzMjKEdkVHRwuzZsry6tUr0faU+Z5e1WOWyWTw8PCAr68vcnJyhGPo1q0b6tWrpzB7x9bWFn5+fjh37hxcXFxw5swZYXlFIiIiFIo/6ujoKDXC1NzcXJQuZvr06fjyyy9x5swZaGtrY+HChaL15eegoocQpWsMlHfvKVN74HX6allK3+fy3zFvsl4TgDJT+VW1XlNFGPQlAvDVV1+hYcOGCAkJgY+PDwwMDNC+fXtMmjQJ586dw+HDh4XcK9Vt3rx5MDIywokTJ3Ds2DE0aNAAM2fOxLNnz+Dt7a10kGjevHmIiorC+fPnUadOHfTp0wdTp04VJaFXUVHBihUr8Oeff+LUqVM4ceIE1NXVYWJigrFjx5abs0oZrq6uaNu2LY4ePYpz585BIpGgTZs2mDdvnlD5Um706NFo0KABvLy8cOrUKdStWxdOTk6QSqXYtGnTa7eBPiyhoaGikWUaGhpo3LgxRo8ejTFjxpQZ/JBPcTx48CCCg4NRUFCApk2bwtXVFaNGjarwC/LrfK6oqqrCysoKvr6+wkg5Zamrq2PDhg1Ys2YNfH19YWBgAEdHR6HKr5yxsTG2bduGvXv3IiwsDBEREahfvz6GDBmCefPmlVtgrTL16tXD5s2b8fvvvyM4OBhHjx5F3bp1YWNjg8mTJ4vOr1Qqxa+//oq9e/fi/Pnz8PX1RZs2bfDrr79izZo1Cj/eiGqKevXqYcuWLdi3bx8uXLgAHx8f1K1bF3369MHnn38uqnJub2+PevXqwcfHB/7+/sjJyUHz5s0xe/ZsjB49WlivcePGmDp1Kg4fPoyjR4+iRYsWlQZ9+TeciEqys7NDy5Yt4ePjIyriFhQUhGfPngmfORUV95KPYpUHSEvKzc1FnTp1oKqqKsrjX9aD2vT0dKUDjlVRVi7z3NxcyGSySvOjV1Z7QB5EfRvtLsnIyAhLly7FV199hZiYGISHh+PEiRPw9fWFjo4OXF1dheuwYcOGSn97VlQnoazrWBZNTU3k5uaWWTOmZN5Tb29vbNu2Debm5pg8eTLatm0rjLhcuHAhEhMTK91XRe0tKioqM9f7gAEDMGDAAKSnpyMiIgIXLlzA2bNn8d1336Fly5aiPLBlqazmRd26dYX+MWXKFEyfPr3S46iKqh7zxo0b4ePjg0GDBsHe3h5t2rQR+uXEiRMVgr4WFhZo2LAh/P394eLiAn9/f5iYmJQ5srikyMhI7Ny5U7TsddMKaGpq4ocffoCjoyN8fHyEQoly8vN78ODB13rYXFVvoq++C/KHUGXdq1Wt11QRBn2pVpFPmy6Lubm5Qp4mOW1tbbi5uQkJwUtq3769aLmdnZ3CdMgDBw4ovc/SbayoXUuXLsXSpUuFf6elpUEqlWL8+PGivFgAhAq35T3NLE1NTQ0LFiwos2poSRKJBA4ODnBwcKhwvbLOi1zp45AbMmSI0jlL+/fvX+YP1LFjxyr1fvpwOTk5KVXdtzy9e/cWcjCVp0mTJgr3cVU+V0qKj49Ho0aNKi0QUJbWrVsrFUQxNDQUjQiUK13IraLP1LI+t3R1deHq6gpXV9dK26ChoQEXFxdRgQUAZU6rI3rXKur7enp6Svdza2trWFtbV7reZ599hs8++0zp9vFvOBGVNHz4cNStWxc+Pj5wdHQUAmHh4eFKFXED/q1lcOvWLVFRNKA4iLJjxw6sX78ebdu2RXBwMKKiohTy5j548ABpaWn46KOP3tCR/aus+iU3b95EUVFRpTmLtbW1YWRkhPv37+P58+cKwWp57YHKHrj9L0JCQhAeHi7kzjUzM4OZmRmGDx8Oe3t7oQ3yh4cxMTEKQd9Xr15h586d6NChA2xsbIT8x9euXVNYt6zc72Vp3bo14uLicPv2bYXzuHjxYsTExMDX1xdnz56FRCLBzz//LBoxXVRUhHv37gn/XZFmzZpBR0cH169fV3jt7t27ogBtXl4evLy8oK6ujvHjx0NXV1cIABsZGcHT0xPXr1+vNOiblJSkUHhUXvPC1NRUVPOivHPm6ekptKOqo7yrcsxAcY5lfX19/PDDD6KcxTKZTJixWFRUJLxWp04dDB06FPv370dgYCBSUlKE3M8VmTp1qlKfC8qqX78+vvzySyxbtgzu7u7o1auXkN6jdevWCA4ORkxMjELQNykpCb6+vujWrVuV0tpV5E301XdBfv/evHlT4f69efPmG9sPC7kRvUfOnj2LkSNHCoVh5JKTkxESEgITE5MypwcQ0fvh77//RnR0NIYNG/ZaxZ2IiIjowxQXFwc1NTVhxH9hYSESEhIUArPlsbKygqamJg4dOiRKh/Xq1Sv4+vpCS0sLZmZmsLGxgUQiwd69e0UpWrKzs/Hbb78BwGvlC6/MzZs3RSkPMjMzsXXrVtSpU0ep/dnZ2UEmk2HDhg2iHOqxsbE4fPgwdHV131jQqSz379/H0aNHFaa4P3z4EMC/+WGtrKygra2Nffv2KRSM2rRpEw4dOoQHDx4AKC4sVa9ePRw+fFi07r1798osplcW+cyybdu2iQKQN27cQGRkJDp16iTkUi0oKMDz589F79+9e7dwDJXlpldVVcWQIUOQnJyMP/74Q1iel5cn5ISXk0ql8PPzg6enp0K6EHn/VCanbl5eHjw8PIQgX1FREbZu3Yrs7GwMHz4cQPEI7G7duiEsLAwBAQGi958+fRq7du1CeHj4a6X1qMoxA8UPdXNzc0XpDgoKCrBu3Trh+pQ+z/L+7+7uDhUVlSrPFnxT5EH5nJwc/Prrr8Jy+WfGtm3bRINL8vPzsXbthXpk8QAAIABJREFUWnh7ewupDt6EN9FX34X+/ftDT08Phw4dEn2WPnnyBPv3739j++FIX6L3iLW1Nfbt24fffvsNYWFhaNq0KdLS0hASEoK8vDzMmzevuptIRK/B3d0dkZGRSExMRN26dUVTv4mIiIgqExcXh1atWgn1PeQFpJQN+urp6WHBggVYtWoVpkyZIgSBAwICkJaWhlWrVkFNTQ1NmzbFnDlzsG7dOiFnuaamJsLCwpCSkoKPP/640nyir0NXVxcrVqyAv78/DA0NERoaipSUFEyZMkWpY5w0aRIuX74MPz8/3LlzB927d8fz588RHBwMAPj+++8rzWf8v/jkk0/g6+uLzZs3IyIiAm3atMHz58/h7+8PTU1NYbaHrq4ulixZgu+++w5OTk6wsrJCgwYNEBkZiVu3bqFDhw6YMGECgOJp80uWLMHy5csxffp0oUBvQEAA6tWrp5AntSy9evXCiBEjcPz4cTg6OqJXr17IysrCuXPnoKWlJeRntbGxQXR0NGbNmgVra2tIpVJEREQgNjYW+vr6eP78uZAmoyIuLi64cuUK3N3dER4eDhMTE1y5cgWvXr1SyNc6c+ZMLF26FM7Ozhg0aBD09PQQExODq1evwtzcHBYWFpXuT1VVFadPn8bt27dhZmaG6Oho3LhxA927dxelIPjqq6/g6uqKr7/+Gr1790arVq1w//59XLx4EXp6egp5aquiKsc8dOhQeHt7Y+rUqbCyskJBQQHCw8Nx//591KtXDy9evMDLly9FOV9btmyJdu3aITY2Ft27dxcKxVaHL774AleuXEFYWBjOnj2LIUOGoHnz5nB1dYW7uzsmT56M/v37Q1dXF2FhYUhMTISlpSVsbGzeWBveVF992zQ1NbFw4UJ89913cHZ2xoABAyCRSBAUFCSs8yZqSjHoS/QeMTQ0xNatW+Hl5YWIiAiEhoZCR0cHH330ESZNmgRTU9PqbiIRvYYGDRogJSUFxsbGWLRo0VvPKUdERES1S3x8vCgtQ3x8PAAoHfQFgGHDhqFhw4b4/fffERAQgPz8fJiammLJkiVC0UagODVL8+bN4eXlhaCgIBQVFcHExASfffYZPvnkkzd3UCV069YNQ4YMwfbt2xEWFgYTExN88803SgeL1NXVsX79enh5ecHPzw9//vkndHR00K9fP3z22Wdv/XeUnp4eNm3ahD179uDy5cuIiIiAlpYW+vTpAycnJ1GaAmtraxgaGgr1F3JyctCkSRNMmTIFEydOFNVf6N+/PzZs2IAdO3bg/Pnz0NDQwMiRI9GhQwd88803SrVtyZIlaN++Pf78808cO3YMampq6N27N2bOnAkjIyMAxXnqi4qKcPToUfz111/Q0dGBsbExVqxYAXV1dSxZsgQXL15Ep06dKj0PW7duxfbt2xESEoKoqCh07twZK1euVEj51b9/f6xZswb79+9HaGgoMjIy0KhRIzg5OWHy5MlKBcQ0NDSwadMmrF69GkePHkWDBg3g7OyMzz77TJTDuEWLFti5cyd2796NS5cu4erVqzAwMICNjQ2cnJz+p1y0VTnmGTNmQEtLC2fOnIGPjw/09fVhYmKC+fPn4969e1i/fj0uXbqkcJ8NHjwYsbGxb+WBS1U0bNgQLi4uWLt2LdavX49evXpBT08P48ePh7GxMf744w8EBgaisLBQeIBkb29faSHBqnhTffVd+Pjjj6GhoYE9e/bg7Nmz0NDQwODBg9GtWzd88803FRbBVJZKUU1IZvEO5ebmKiS/rglK51EketPYx95/Dg4OkEqloqlBRG8SPyfef/LcreVVZ69O7F/0NrF/VY0812JtER0dXa2j2+jtefjwIcaMGYP+/fvjv//9b3U3h94jDg4OyMjIwJkzZ6q7KW/dd999h5CQEBw7duytjlinNyczMxNZWVlo0KCBQlq/EydOYNWqVfj+++8xePDgSrf1+PFjmJmZlfkac/oSERERERERERG9Z+7cuYOgoCAMHjyYAd/3yP379zFq1CisWrVKtFwmk+HIkSOQSCTo0qXL/7wfpncgIiIiIiIiIiJ6T+zfvx9+fn64d+8eVFRUhLzQ9H5o164dOnbsiJMnT+Lhw4fo2LEjcnJycPHiRTx8+BAuLi5o2LDh/7wfBn2JiIiIiIiIiIjeEw0aNMDDhw/RsGFDzJ07F82bN6/uJlEV1KlTB2vXrsUff/yBgIAAHDlyBFKpFK1bt8bs2bMxaNCgN7IfBn2JiIiIiIiIqMZp0qQJQkNDq7sZ9B6qifUN3iQbGxulCxlSzaSjo4Np06Zh2rRpb20fzOlLREREREREREREVIsw6EtERERERERERERUizDoS0RERERERERERFSLMOhLREREREREREREVIsw6EtERERERERERERUizDoS0RERERERERERFSLMOhLREREREREREREVIsw6EtERERERERERERUizDoS0RERERERERERFSLMOhLREREREREVAukp6ejX79+OHbsmLBs48aNsLW1RVFR0Wttc926dbC0tERERMRrtys8PBy3bt167fcrSyaTwdvb+41vNz09HZaWlnBzc3vj26bynThxApaWljhw4EC17D84OBiWlpbYsWNHtez/ddjY2MDBwaG6m/FOfYjHrCwGfYmIiIiIiIhqgZs3b6KoqAgdO3YULevYsSNUVFSqpU1Hjx7FggULkJqa+tb35ebmht27d7/1/dC70bZtWzg7O8PMzKy6m0L0XlKt7gYQERERERER0f/u1q1b0NTURMuWLQEABQUFuH37NsaPH19tbXr27Fmt3Be9faampjA1Na3uZhC9tzjSl4iIiIiIiKgWuHXrFkxNTSGRSAAAiYmJyM7OFo38JSKiDwNH+hIRERERERG9p9zc3BAZGSlaZmlpKfr3okWL0LhxYxw5cqTCbR0/fhyHDh1CUlISGjZsiAkTJpS77oMHD7Bjxw78/fffyMjIgJGREWxtbTFx4kSoqqoqtG3p0qUAgNDQUGEb/v7+OHDgAO7cuQMVFRV06NABjo6O6NGjh8L+QkJCcODAAcTFxUEikaBt27ZwcnJCt27d8PDhQ4wZM0Z0/HZ2dvj6668BAJmZmdi7dy/8/f2RmpqKunXrol+/fpg2bRr09fVF+3n48CG2bduGv//+Gzk5ObCwsMDUqVMrPG+lxcfHY+/evYiMjER2djaaN28OBwcHDB8+XJRm49atW9izZw+ioqKQk5MDIyMjDB06FBMmTICampqwnpubG548eYINGzZg06ZNuHz5MgDAwsICCxcuhKqqKjZv3oygoCAUFhaiS5cu+OKLL9CkSRPRORk6dChGjBiBLVu24M6dOzAwMMDQoUPh6OgIdXV1hfPXvHlzeHl5AQCcnJwwfvx4FBYWwtfXF76+vrh37x6kUik6dOiASZMmoWfPnqLzOGbMGEyfPh0mJibYs2cPEhMTUb9+fYwdOxbjx4/HtWvXsGXLFty+fRv6+vqws7ODo6Oj0H9OnDiBVatWYe7cufj0008BFI/m9vDwQGRkJFJTU6Grq4uPPvoIzs7OaNasmeg6KNNH5f755x/s3LkTMTExUFNTg42NDdq3b1+l615RH5XLz8+Hj48Pzp49i3v37kEmk8HAwAC9e/fG9OnThf4oP39TpkxBRkYGjh8/DnV1dXz55ZewtrbGixcvsGPHDgQHByM9PR2dO3fG3Llzy2xXeHg49u/fjzt37iA7OxtNmzbFkCFDMGHCBEil0gqPyc3NDQ8ePMDWrVuxZs0aREREQENDAz179oSLiwsaN24sWl/Ze23lypU4deoUPD09sXLlSqSkpMDU1BRbt24tNxVNVY45KysLBw4cQGBgIJKTk1FQUABDQ0NYWVnB2dkZmpqayMnJwSeffAJdXV0cOXJEYb+rVq3CiRMn8Mcff6B58+YVnqeaqsYEfXNzc2Fvb49ly5ahb9++AIBLly7h119/RUJCAgwNDTFt2jSMHTtWeE9YWBh+/PFH3L9/H126dMHKlSvRokWL6joEIiIiIiIionfKzs4O5ubmePXqFQ4fPoyPP/4YxsbGAIALFy7g0aNHGDNmDHR0dCrczrZt27Bnzx40btwYI0aMwLNnz7BmzRrUq1dPYd3Y2FjMnTsXMpkMAwYMQOPGjREVFQUPDw9ERUXhl19+gUQigZ2dHQAgMjISgwcPFv1e3759O3bv3o0mTZrAzs4OKioqCAgIwPz58/H111/DxsZGWHfv3r3w8PCAvr4+Bg4cCDU1NZw9exZz587F6tWr0bFjRzg7O+PgwYPIzc3F5MmT0bZtWwBARkYGZs2ahYSEBHz00UcYOHAgUlJScOzYMYSFhcHDwwMNGjQAADx58gQzZszAs2fP0K9fPzRu3BhhYWFYsGCB0tfj6tWrWLRoEQoKCmBlZYVGjRrh4sWL+Omnn/Do0SNMmzYNQHGRsK+//hoSiQT9+/dH/fr1cfXqVWzbtg3h4eFYt26dKPCbmZmJmTNnomHDhhg5ciSioqIQEBCAFy9eIDs7GzKZDHZ2drh79y5CQ0Px9OlT7NixQxTIio+Px8KFC9GpUyc4ODjg6tWr2LNnD27cuIF169ahTp1/J4OHh4cjODgYw4YNQ1paGjp16oTCwkJ8++238Pf3h5GREUaMGIGsrCxcuHABCxYswIIFC2Bvby86H4GBgUhMTMSgQYPQtWtX+Pn5wd3dHY8ePYKvry969+6N0aNHw9/fHzt37oSuri7GjRtX5rmVyWRYuHAh7ty5gwEDBmDQoEFISUnBuXPnEB4eDm9vb+jp6VWpjwLFsaXFixdDTU0NAwcOhEQiwcmTJ3H27Fmlr3tlfbRXr14AgG+//RaBgYHo0qULRo4cidzcXFy+fBm+vr6IjY1VKBonL8o4evRo3Lt3D2ZmZsjKysLs2bORmJiIjz76CK1atUJkZCRmz54NmUwmutejoqKwePFi1K1bF4MHD4a6ujr+/vtveHh4IDk5WXgYU5GcnBzMmTMHEokEo0aNQmJiIs6cOYOIiAhs374dDRs2BFC1e03uq6++QocOHWBhYQEtLa1yA75VOeb8/HzMmzcPt27dQq9evdCrVy+hn3p5eSElJQU//vgjNDQ0MHDgQJw8eRJRUVGi4LxMJkNgYCDMzMze24AvUEOCvvIbNy4uTliWmJiIGTNmwNXVFXZ2doiKisLy5cthYGAAa2trPHz4ELNmzYKrqysGDRqETZs2wdXVFX/99Zfog4qIiIiIiIg+TAcOxMDL61Z1N6NCEyd2wKefVm1EYUnDhw8HUBzgPXz4MJydnYXg6uXLl9G5c+dKR6omJSVh3759aNu2Ldzd3aGrqwugeFTu4sWLResWFRVh5cqVyM3NxdatW0WjITds2IADBw7A19cX9vb2GD58OB49eoTIyEh8/PHHsLKyAlBcXG7Pnj0wNzfHr7/+Cg0NDQDA1KlT4eLigl9++QU9e/aEvr4+7t+/D09PT7Ro0QLu7u4wMDAAAHz66aeYMmUKNm3ahL1792Lq1Kk4efIkMjIyRMfr4eGBhIQELFy4UBSQDAkJwZIlS7B+/Xr88MMPwrppaWlYvnw5hg0bBgDIzs7GokWLkJaWVum1KCgowH//+18UFRVh06ZN6NSpEwBg+vTpcHFxwd69e+Hg4AA1NTWsWrUKGhoacHd3R7t27QAUB6t+/PFH+Pn5Yf/+/XBychK2/eLFC1hZWWHVqlVQUVFBfn4+xo0bh8jISHTu3Blbt24VRm3KR1jfu3cPJiYmwjYSEhJgb2+PhQsXCvv7z3/+g+DgYJw6dUroS0DxiNqff/4Z/fr1E5adPn0a/v7+6NWrF3788UdoamoCAJKTkzFr1iysW7cOvXr1QtOmTYX3xMXF4aeffhKufZ8+fbBgwQIcOnQICxYsgIODAwDAwcEBY8aMwdmzZ8sN+l65cgW3b9+Gk5OTEDwHAC8vL2zatAlnz56Fg4NDlfpoQUEBfvvtN0ilUnh4eKBVq1YAgMmTJ2PWrFmVXnMASvXRXr164caNGwgMDMTQoUPx7bffCu/Pz8+Hs7MzYmJicP/+feGhDQA8f/4cu3btEh5iAICnpycSExMxdepUODs7C9tYuXKlQqD64MGDyMvLw5YtW2BkZCSsO23aNJw6dQpz5syp9IFQeno6mjVrhk2bNgkjwr29vbFx40Z4eHgII+qrcq/Jde7cGatWrar0HHt5eSl9zIGBgbh58yYcHR3h4uIiLJ81axbGjx+PkJAQ5OTkQENDA7a2tkKAv2TQNzQ0FJmZmbC1ta20bTVZtUdH4+PjMW7cONy/f1+0/OTJk+jQoQNmzpyJFi1aYOTIkRg1ahT++usvAMUdt3379pg+fTratGmDVatW4eHDhwgLC6uOwyAiIiIiIiKqNjExMdDW1hYCRvn5+YiLi1Mqn6+/vz8KCgrg6OgoBHyB4mn+8hGKctHR0UhISMCIESMUpr9Pnz4dUqkUJ06cqHB/x48fR1FREWbPni0EfAGgbt26mDRpEnJycuDv7w8ACAgIQEFBAaZMmSIE0wCgWbNmcHNzw7Bhw5Cfn1/mfvLz83Hq1Cm0bNlSYQRq//790aVLFwQFBSEzMxN5eXkICgpCy5YthYAvAGhqaiod/IuOjkZKSgpsbW2FgC8AqKurw83NDVOnToVMJkNISAjS09MxduxYIeALAKqqqpg7dy7U1dVx/Phxhe2PGzdOGAmpqqoqXNsxY8aIpumbmZkBKE4RUJKmpiamT58u2t/s2bMBAH5+fqJ11dXV0adPH9GykydPAgAWLlwoBHwBoGnTpnB0dERBQQFOnz4tek+TJk2EgC8AdOnSRWjLqFGjROvVr19foc0lFRYWAgBu374NmUwmLLe3t4ePj49wjavSR2/evImUlBTY2dkJAV+guH/JU0pURtk+amhoiOXLl4sC1kDxdZCfl+fPn4tea9asmSjgCwDnzp2Drq4uPv/8c9E23NzcFEbKys/ZtWvXROv+9ttvOHnyZKUBX7kZM2aIUoCMGzcOTZo0QWBgIHJzc6t0r5U0aNAgpfZflWM2NTXFkiVLFK6ftrY2TE1NUVBQgFevXgEAunfvjsaNGyMgIED0OXLmzBmoqqpi8ODBSrWvpqr2kb5XrlyBpaUl5syZI4qq29nZiT4YAEBFRUW4saOiomBhYSG8pqmpCTMzM0RGRgrpIYiIiIiIiOjD9emn7f+nUbTvk1u3bqFdu3ZCACQhIQEymQwdOnSo9L3x8fEAUGYO006dOokGV8XGxgIAUlJSFKaiA4CWlhbi4+NRVFRU7lRt+TYCAwNx8eJF0WtPnjwBAGEmsLxtJYOociWDhmW5f/8+srOzUVhYWGZbZTIZCgoKcOfOHejp6SE7O7vMc9C+fXuFHLBlqaitFhYWQgxDfmwlYyBy+vr6MDY2RlxcHDIyMkRBudI5a+UB85K5ewEIaSHy8vJEy9u0aSOkPyi5TT09PaHtcoaGhkL6A7m4uDg0bNhQNJJXTh60LL2d0uvKg8VlbV9NTQ0ZGRkK25azsLCAkZERQkND8cknn+Cjjz5C7969YWlpiUaNGgnrVaWPVtb3laFsHzU0NBQCwLGxsbh//z6Sk5Nx+/ZtXLlyBcC/QVq50tdWJpMhKSkJ5ubmCn2yQYMGaNKkiWgbI0eOREhICH744Qfs3r0bvXv3Ru/evdGjR49K8/nKqaioKPRViUQCU1NTBAUFITk5GUVFRUrfa/K+UtbxlaWqx2xsbAxjY2PIZDJER0cjKSkJDx48QGxsrJBjXL6+iooKbGxssGfPHly+fBl9+/bFq1evEB4ejj59+qBu3bpKnaOaqtqDvuPHjy9zecuWLUX/fvr0KU6cOAE3NzcAQGpqKgwNDUXrGBgY4PHjx2+noUREREREREQ1TOlCbqWLuMnz0VZUyC09PR1AcTCstNJBQnlQLiwsrMKZtllZWdDW1i7zNfk29u3bV+775SPx5P9f3rYqIt/PvXv3sHPnzgr3JQ9Ql3UOJBKJUiMiKzqPJclHO5a3zQYNGiAuLg45OTmidUqOii5J2eCdPPdqafXr10dycrJoWclRnSXbXb9+/XLbDBTnfy2p5IjgkpRtc0kaGhpC7ml/f38EBQUhKCgIderUwYABA/DVV19BT0+vSn1U3r+U6fvlqUof/fPPP7Fr1y48ffoUAKCrqwszMzO0aNECN2/eRFFRkWj90tehovbK2/zixQvh33369IG7uzu8vLxw5coVHDp0CIcOHYKenh6cnZ1FdbPKU7du3TKvl3xUc2ZmphBEVeZeK6lk3urK3qPsMRcWFuL333+Ht7e3cE/q6+ujc+fOaNKkCRITE0Xn2dbWFnv27MHZs2fRt29fnD9/Hnl5eaK84u+rag/6KiMrKwtubm4wNDQUgsTZ2dkKnUNNTQ25ubkVbksikYiG29cUqqqqNbJdVHuwj73/pFIpVFRUeB3preHnxPtP/oW8Jl5H9i96m9i/6ENmZ2eHli1bwsfHR1TELSgoCM+ePcPo0aMBlB9gBCCkdMjIyFAY2ZadnS36tzyIt3TpUowYMeK12qypqQmJRAJ/f/9KR9DKAz1ZWVkKbZPJZJBKpeXW9ZG31dbWFv/5z38q3E9iYiIAKEw/ByCMYqyMfH9ZWVkKr+Xn56OoqAhSqVQ4JnngrzR5oErZoKOySqZEKKms614WLS2tcnMby9v8tkdG6uvrY/78+Zg3bx7i4+Nx+fJlnDp1CgEBAVBRUcEPP/xQpT4qP8dlXXdlrjmgfB/19/fH6tWr0aZNG3z55ZcwNTUVRiivXr0aN2/erHRf8vaWNyK6rDabm5vD3Nwc2dnZiIqKQmhoKE6dOoV169ahWbNmCmk8SisvzlbymsuD/crca1VV1WP29vbGtm3bYG5uLhR1lH9HWLhwoXCvyxkbG8PMzAwhISHIzc2Fv78/dHV1FR6gvY9qfNA3PT0dM2bMwIMHD+Dl5SXcvOrq6godLzc3t8zKoiUVFBQolYD9XTMwMKiR7aLag33s/ZeXlwepVMrrSG8NPyfef/JpnDXxOrJ/0dvE/lU1ykynpffH8OHDUbduXfj4+MDR0VHISxoeHq5UETegeGq7v78/rl+/rjAdPyYmRvTv1q1bC8tLB9Ty8/OxZcsWNG7cWBhBWFaKh9atWyMuLg63b99WyDl848YNhISEoE+fPujWrRtatWqF4OBg3Lx5U6Hvrl27FmfOnMH+/fthZGSksC9jY2OoqakhJiamzHQTBw4cQHZ2NkaPHo1mzZpBR0cH169fV2jv3bt3yw2YlnVubt26JSqKBgDnz5/HypUrsXz5ciFHa1RUFPr37y9aLzMzE3FxcWjWrJlSoyCrIiYmBoWFhaIg+aNHj/D06VOF9Jpladu2LSIiIpCQkCDKfwsA//zzDwDFWdtv0j///IPAwECMGTNGyHXbtm1bODg4YMSIEYiKigJQtT4qz6l8/fp1hXVL9/3yKNtH5QXHvv32W4Xzd+/ePQBQGOlbmrq6OkxMTIS8xiVHAqenp+PBgweiEd0HDx7Eixcv4OLiAk1NTSG9g5mZGX744QdERUVVGvTNysrCvXv3hAKRctHR0ahXrx6MjIyQn5+v9L1W1QcDVT3ms2fPQiKR4OeffxaNvi4qKir3PNvZ2eHXX39FSEgIrl27huHDh7/x+686VHsht4o8e/YMn3/+OZKSkrB3715RBcNGjRohNTVVtP7Tp0/Lna5AREREREREVBvFxcVBTU1NCMoUFhYiISFBoQBUeQYPHgw1NTXs3r1b9ADln3/+QUhIiGjdbt26wcjICMePH8eNGzdEr/3+++/4448/hJyqAIS8rSXzy8oLpW3YsEE0wjIzMxOrV6/Gvn37hOniQ4cORZ06dbB37168fPlSWPfBgwfw9/eHkZERjIyMABSP+i9ZjEldXR3W1tZITEzEH3/8IWprREQENm3ahBMnTkBXVxeqqqoYMmQIkpOTRevm5eVh69atypxGdO3aFY0aNcLp06dx+/ZtYXlubi4OHDgAFRUV9OjRA1ZWVtDR0YGPj4/oXOXn52PdunWQyWSwtbVVap9VkZaWBi8vL9H+NmzYAABKjdqWX7d169aJRlempKRg9+7db73wVVpaGg4dOqRwLZ89e4bc3Fw0btwYQNX6aIcOHWBiYgI/Pz9RwP/p06cK+ymPsn1UHkQsXazt1KlTQoqW8ooSljRs2DBkZ2djy5YtQvCyqKgIHh4eKCgoEK0bHh6OvXv3KpwHecE8+TmrzObNm0X3sLe3t1AATyKRVOleex1VOWY1NTUUFBQonOfdu3cLx136PA8ePBhSqRSbN29Gfn7+W7n/qkONHembm5uLmTNn4vnz59i/f78o4AsUf5jKE10DxcO5b968qXRVTSIiIiIiIqLaIC4uDq1atRICrMnJycjOzlY66Nu4cWO4ublhzZo1cHJywoABA5CRkYGAgAA0atRIlO9VIpHg66+/xsKFC+Hq6or+/fujadOmiImJwdWrV2FkZISZM2cK68sHZu3Zswe3b9+Gs7MzevTogbFjx+LQoUOYPHky+vTpAzU1NQQHB+Px48cYNWoUunfvDgBo0aIFnJ2d4enpCUdHR1haWqKoqAjnz59Hbm4uli1bJuyrQYMGSEpKwooVK9CzZ0/Y2dnBzc0NN27cwMaNGxESEoKOHTsiNTUVQUFBkEgkWLp0qTDy1cXFBVeuXIG7uzvCw8NhYmKCK1eu4NWrV0qN+lNVVcXSpUvx1VdfYebMmRgwYAD09fVx8eJFJCUlYe7cucL5WLp0Kb755hvMnDkTVlZW0NfXR0REBO7cuYOuXbti8uTJSl27qtDS0sL27dtx9epVmJiY4OrVq7hz5w5sbW2Vmspua2uLCxcuIDAwEJ9//jl69+6N7OxsXLhwARkZGViwYIFCsbk3ycrKCp06dcLRo0dx584ddOrUCZmZmQgMDAQAYVR7Vfq3YzjoAAAgAElEQVSoiooKli1bhnnz5mHu3LkYOHAgtLW1ERQUVG4+4tKU7aM2NjY4d+4cli5dio8//hja2tq4desWIiMjoa+vj+fPnyvkvC3LuHHjcOHCBRw6dAi3bt2CmZkZbty4gYSEBOjr64vWnTZtGiIiIjBnzhxYW1ujQYMGSExMRGhoKExMTJTOW3v9+nVMnToVPXr0QGJiIi5fvoyWLVvCyclJWKcq91pVVeWYbWxsEB0djVmzZsHa2hpSqRQRERGIjY0t9zzr6enB0tISgYGBMDIyEhWbe5/V2JG+u3fvRnR0NH766SdoamoiNTUVqampQnJmBwcHREVFYcuWLYiPj8fy5cthZGRU6bB0IiIiIiIiotokPj5eFOCNj48HAKWDvkDxb+yffvoJjRo1wokTJxAVFYVp06bB3t5eYd2uXbti+/btsLa2RlRUFA4dOoRHjx5h7Nix8PDwEIp6AcUj6KytrZGcnIyjR4/i0aNHAID58+fjm2++gaGhIc6cOYOTJ0+ifv36WLZsGRYuXCjan5OTE77//nsYGhri9OnT8PPzQ4cOHbBx40aYmZkJ67m6uqJly5YICAjA6dOnARTngN2+fTsmTJiA1NRUHD58GFFRUbC0tMS2bduE4DJQHPjZunUrRo0ahTt37sDX1xf169fHhg0blJ7qbWFhgS1btuCjjz7CpUuX4OPjAw0NDXz99df49NNPhfUGDhyILVu2wMLCAuHh4Th27BgAYPbs2diwYcNrFTqrjJGREdasWYNnz57hzz//REFBAebNm4evv/5aqffLc+bOnz8fWlpaOH78OEJDQ2FmZob169eX2VfeJKlUitWrV2Py5Ml48eIFjhw5An9/f3Ts2BEbN25Er169hHWr0kfNzMywdetWWFhY4OLFizh37hz69u0reqBQGWX6aN++fbFixQo0bdoUfn5+OHnyJGQyGRYuXIg1a9YAAC5duqTUeVi7di0mT56M1NRUHD16FEDxCOzS+e07dOiAzZs3o2fPnrh69SoOHDiAO3fuYOzYsdi8ebPSge0NGzbAwMAAvr6+uHv3LsaOHYstW7aI0idU5V6rqqocs729Pb744gvo6enhr7/+gp+fH7S0tLBixQosXrwYAHDx4kWFfVhbWwNArSjgJqdSVFnCkHeoXbt22LVrF/r27Qt7e3tER0crrNO9e3d4e3sDKE5M/9NPP+Hhw4fo2rUrVq5cqTAiuLTc3Nwame+LecjobWMfe/85ODhAKpUqPc2IqKr4OfH+c3BwAIByq7NXJ/YvepvYv6qmtuX0jY6OFoohEVHZLC0t0aZNG+zZs6e6m0LvETc3N0RGRuL06dOvnZrhfbF161b8/vvvOHDgwFsdsf6mPX78WPTwq6Qald6hZC4bHx+fStcfMGAABgwY8DabRERERERERERERLXUkydPcPz4cXTv3v29CvhWpkYFfYmIiIiIiIiIiIjetjNnzsDb2xtJSUmQyWRCXujagkFfIiIiIiIiIiIi+qAYGhri8ePH0NbWxrx589CtW7fqbtIbxaAvEREREREREVEtFhoaWt1NoPfQxo0bq7sJb5W5uTlOnTpV3c14a+pUdwOIiIiIiIiIiIiI6M1h0JeIiIiIiIiIiIioFmHQl4iIiIiIiN5rRUVF1d0EIiKid6qyv30M+hIREREREdF7SyKRoKCgoLqbQURE9E4VFBRAIpGU+zqDvkRERERERPTe0tPTQ3Z2dnU3g4iI6J3Kzs6Gnp5eua8z6EtERERERETvLQMDA8hkMqSnpyM/P5+pHoiIqNYqKipCfn4+0tPTIZPJYGBgUO66qu+wXURERERERERvlLq6Olq2bIm0tDS8ePGCqR6IiKhWk0gk0NPTg5GREdTV1ctdj0FfIiIiIiIieq+pq6vDyMgIRkZG1d0UIiKiGoHpHYiIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGoRBn2JiIiIiIiIiIiIahEGfYmIiIiIiIiIiIhqEQZ9iYiIiIiIiIiIiGqRGhP0zc3NxYgRI3Dx4kVhWXJyMpydndGtWzfY2dkhKChI9J6wsDB88skn6Nq1Kz777DPcu3fvXTebiIiIiIiIiIiIqEapEUFfmUyGBQsWIC4uTlhWVFQEV1dX1KtXD4cPH8bo0aMxd+5cJCUlAQAePnyIWbNmYeTIkThy5AgaNGgAV1dXFBYWVtdhEBEREREREREREVW7ag/6xsfHY9y4cbh//75oeVhYGO7evYvvv/8ebdq0gYuLC8zNzXH48GEAwMGDB9G+fXtMnz4dbdq0wapVq/Dw4UOEhYVVx2EQERERERERERER1QjVHvS9cuUKLC0tceDAAdHyqKgodOzYETo6OsKyHj164J9//hFet7CwEF7T1NSEmZkZIiMj303DiYiIiIiIiIiIiGog1epuwPjx48tcnpqaCkNDQ9EyAwMDPHr0qMLXHz9+/HYaSkRERERERERERPQeqPagb3mys7MhlUpFy9TU1JCXlye8rqampvB6bm5uhduVSCQwMDB4s419A1RVVWtku6j2YB97/0mlUqioqPA60lvDz4n3n/y7U028juxf9DaxfxERERGJ1digr7q6OjIyMkTLcnNzoaGhIbxeOsCbm5uLevXqVbjdgoICpKWlvdnGvgEGBgY1sl1Ue7CPvf/y8vIglUp5Hemt4efE+0/+cLwmXkf2L3qb2L+qpkmTJtXdBCIiInrLqj2nb3kaNWqE1NRU0bKnT5+iYcOGSr1ORERERERERERE9CGqsUHfrl27IiYmBllZWcKyq1evolu3bsLrERERwmvZ2dm4efOm8DoRERERERERERHRh6jGBn179uwJIyMjLFmyBHFxcdi2bRuioqIwduxYAICDgwOioqKwZcsWxMfHY/ny5TAyMkKfPn2queVERB+YwgKgqKi6W0FERERERERE/1+NDfpKJBJs3rwZz549g729PXx9fbFx40Y0a9YMANCsWTO4u7vD19cXDg4OePr0KTZv3ow6dWrsIRER1T4FeWiw3xp6gcuquyVERERERERE9P/VqEJusbGxon+3aNEC+/btK3f9AQMGYMCAAW+7WUREVA6NuL8gfRYH1ed3kNX5c+Q36FDdTSIiIiIiIiL64HFYLBERvZ6iIuhc2Yj8eq1QpKYL3dBV1d0iIiIiIiIiIgKDvkRE9JrUE89DmhaDjJ7zkWExBxqJ56GWdKG6m0VERERERET0wWPQl4iIXovOlY3I122KbNP/Q2ZXZ+TrNoXuhZVAUWF1N42IiIiIiIjog8agLxERVZk0ORxqKZeR2X0mIJECqhrI6LMYak+uQeO2b3U3j4iIiIiIiOiDxqAvERFVmc7VTSjQrI8sswnCsuz29shr0BG6F/8L5MuqsXVEREREREREHzYGfYmIqEpUn96Cxt1zyOo6FZBq/fuCSh286vcfqL5Kgva1PdXXQCIiIiIiIqIPHIO+RERUJdpXN6NQqoXMrlMUXsttMQAy4wHQ+Xs9VGQv333jiIiIiIiIiIhBXyIiUp7kVRI0Y/9EVqfPUKShX+Y6r/oth0rOC+j8vfEdt46IiIiIiIiIAAZ9iYioCrQjtgIqdZBpPr3cdfIbdkJ2hzHQ/scTdV49eIetIyIiIiIiIiKAQV8iIlJSnayn0Lrhjez2DijUNapw3fTeiwAAumG/voumEREREREREVEJDPoSEZFStKJ2AgUyZPSYVem6hXrNkNltKjRvHYJqavQ7aB0RERERERERyTHoS0RElVLJzYD2/2PvvsObKtswgN8ne4+2FAoIyF4iqwVEZClb9hIVXKCi4Eb8UBREBXGhIALiAlkCogwZIiCI7L0cgLJH2zTNnuf7o4oiBZo26Unb+3ddvWqTc973rj2k6ZM3z7v3U3irdEQooVqeznGmDoOoscD402sxTkdERERERERE/8aiLxERXZfuwGzIfHa4Gj+W53NEtRnO1Ceg+XM9VH9uiGE6IiIiIiIiIvo3Fn2JiOjagj7od02Hr/ytCJRpENGprnqDEDTdANNP4wAxHKOARERERERERPRvLPoSEdE1aY8sgtx1Ds4IVvleolDDcctIKC8ehPaXr6MfjoiIiIiIiIiuwKIvERFdXTgEw84PEUi+Cf4Kt+VrCG/1bvAn14Nh83gg6I1yQCIiIiIiIiL6LxZ9iYjoqjRHV0KRdQzOxo8DgpC/QQQZHLe+BIXjNPR7P41uQCIiIiIiIiK6Aou+RESUO1GEfsdkBM03wlulU4GG8t/QHN5KbWHY/j4Ery1KAYmIiIiIiIgoNyz6EhFRrlQnN0F1YS+cjYcCMnmBx3M0HwXB74Bh+/tRSEckLe2RRTBseUvqGERERERERLli0ZeIiHJl2DEZIX1peGr2jsp4waSa8NTqC/3eTyHPPhmVMYmkIHizYFr3Aoxb34H62Cqp4xAREREREV2BRV8iIrqC8vxeqE9uhKvBYEChjtq4jqbPAoIMxs0TojYmUWHT75kJmd+JoLEczOtGQfC7pI5ERERERER0GRZ9iYjoCvodUxBWmeCue29Uxw0by8LVYAi0vyyG4sK+qI5NVBgEnwP6PTPhrdwOWR2mQO48wzYPREREREQUd1j0JSKiy8htR6H5fTncN98HUW2M+vjORkMR0ibAtHEcIIpRH58olnT7P4fMlwVn6pMIlE2Dq+490O+ZAcWF/VJHIyIiIiIiuoRFXyIiuoxh51RArobr5gdjMr6oNsGZ9hTUpzZB/ef6mMxBFAtCwA39rmnwVmyFQJn6AABH8/8hrEmAee0IIBySOCEREREREVEOFn2JiOgSmfMstIe/grtOP4T1pWI2j/umexE0V4Jx0zgWyqjI0B74EnJPBpxpT166TdRYkN1yDFQX9kK37zPpwhEREREREf0Li75ERHSJfvcMQAzD1fDR2E4kV8HR/AUoMw5De2RhbOciioagF4adH8JX/hYEyqZddpe3enf4KrSE8ecJkDnPShSQiIiIiIjoHyz6EhERAEDwZkG3fxa81bsiZK4Q8/m8VbvAX7oBjD+/CQQ9MZ+PqCB0h+ZD7joPZ+oTV94pCLC3fgNCKAjThtGFH46IiIiIiOg/WPQlIiIAgH7f55AFXHA2eqxwJhQEOFq8BLnzLPS7Py6cOYnyI+SHfsdk+FMawX/DrbkfYqkER5OnoP19OdTHVhdyQCIiIiIiosux6EtEREDADd2ej+Gt1BbBUrULbVp/uabwVm4Hw47JEDwZhTYvUSS0RxZB4TgNZ+qTgCBc9ThXw4cRSKwB0/pREPyuQkxIRERERER0ORZ96ZoErw0Qw1LHIKIY0x2an7NBVePHC31uR/NREAIuGLdNKvS5ia4rHIRh+2QEkm+Cr1Kbax8rV8HeZgIUjtMwbH2rcPIRERERERHlgkVfyl04BOPGsSgzrQ5KfXYLDFvfgTz7lNSpiCgWQgHod06FP6XxFRtUFYZgQjW46wyAbt/nkGf9UejzE12L5tdvobAfh+M6q3z/FiibBlfde6Df/TEUF/YXQkIiIiIiIqIrsehLVxD8TliXPwjDro/gqdETIXMFGLe8hVKfNkHC1/2h+eVrbrpEVIxoflsKheMUnI0fy1NRKxacTZ+BKFPA+PN4SeYnypUYhmH7JAQSa8JXpX2eT3M0/x/CGivMPzwPhEMxDEhERERERJQ7Fn3pMvLsU0j8qjvUx9fC3uo1ZHWYjMyeC3Dhvi1wNnkKctsxWFc+htIfN4TphxegPL8XEEWpYxNRfokiDDsmI5BQHb4b75AsRlhfGq6Gj0D767dQntstWQ6if9P8vgLKzN/gTB0OCHl/yiRqLMhuOQaq83ug2/95DBMSERERERHlLuKi7+7du+Hz+WKRhSSmPLsTifM6QZ59CpndZsF98/2X7guZK8DZ9FlcvH8LMnrMh69SW+gOzUfSvI5I+rIt9LumQ+bmJkxERY36j7VQZhyBq/FjERW1YsHV6FGEtEkwbhrHF5NIeqIIw7b3ELRUhrfanRGf7q3eHb4KLWHcPB4y59kYBCQiIiIiIrq6iP/CHzZsGJ544olYZCEJaY4sRuKi3hBVBqT3Wwp/xVa5HyjI4K/QAlkdJuP84N2wt5kAUaGFaeMrSJ7ZANZlD0J9bDUQDhZqfiLKH8OOyQgZysJTvbvUUSCqDHA2fQbq0z9Dffx7qeNQCac+/j2U6YdyVvnK5JEPIAiwt34DQigI04bR0Q9IRERERER0DREXfR0OB6pWrRqLLCQFMQzDz2/Cuupx+Ms0RHq/ZQglVMvbqWoz3Dfdi4z+y3HxnnVw1X8QyjPbkbD0PiTPbAzjpnGQZ/4W42+AiPJLeXorVGe2wdnoEUCulDoOAMBdZwCClsow/vQaXzwi6fy9ytd0Azw1euR7mJClEhxNnoL29+U5L4gSEREREREVkoiLvm3btsWaNWuQmZkZizxUmAJuWFY8AuO29+Cucxcye8yFqE3I11DBxBpwtHgZFx7cicwunyBQpj70u6YheVZLJC64E9oDX0LwOaL8DRBRQRh2TkFYY4WnzgCpo/xDrkR281FQZv4K7aH5UqehEkp14keozu+Gs/GwAr8g4mr4MAKJNWBaPwqC3xWlhERERERERNemiPSE1NRUbNu2DW3btkWjRo1Qrlw5aDSaK44TBAEjR46MSkiKPpnzLKxL74fywn5kt3gZrgZDAEEo+MByJXxVOsBXpQNkrgvQHlkE7aH5sKx9DuKGl+Cp2gWeOv3hL9dU8v6hRCWZIv0wNMe/h6PpsxCVOqnjXMZXpQP8KY1h3PIWvDV6xF0+Kv4M295DyJACT60+BR9MroK9zQQkfdUdhq1vwdHi5YKPSUREREREdB0RF33HjBlz6b83bdp01eNY9I1fivP7kLD0Pgh+B2x3fgZf5TtiMk9YnwxXo0fhavgIlOd3Q3twPrS/LoHuyEIEzRXhqdUX7lp9EDaVj8n8RHR1+p0fIqzUwVXvPqmjXEkQkH3rS0j6qhv0u6fDmfak1ImoBFGd3gL1ma2wt3wVUKijMmagbBpcde+BfvfH8NToiWDyTVEZl4iIiIiI6GoiLvp+8cUXschBhUTz2zJYVg9HSJuEzL7fIphUK/aTCgICZRoiUKYhsm97GZqjK6E7OA/GLRNh2PIW/BVawF27P7xVOgCKK1eNE1F0ybNPQfvLErjqP5jvli6xFiibCk+VTtDvmAJ33XsQ1iVJHYlKCMPWdxHSJsFdN7ptTxzN/wfN0ZUw//A8Mvouzd/mcERERERERHkUcdE3LS0tFjko1kQRhu3vw/jzBPhTGsHW5VNpiihKHbw1e8Jbsyfk9hPQHl4A7aEFsK4cirDaDE+N7vDU7o9Acr3otJsgoivod30ECLKcti5xzNH8BWiOrYJh6zvIbv261HGoBFCe3Qn1yY3IvvVFQKGN6tiixoLslmNgXfkYdPs/h/vmB6I6PhERERER0b9FXPT9m81mw3fffYcjR47Abrdj0qRJ2LlzJ8LhMFJTU6OZkQoq6IXl+2eh/WUx3DV7wd52YlysqA2ZK8DZ9Fk4mzwN1cmfoDs0H7qD86Hf9zkCibXgqd0Pnpq9ENYlSh2VqNiQuTOgOzgHnpo9ETaWlTrONYWsVeC+6R7oDsyGq/6DCFmrSB2JijnD9kkIa6xw3zQoJuN7q3eH79ACGDePh7dKR4QNKTGZh4iIiIiIKF87aS1duhRt2rTBq6++igULFmD16tUAgPXr12PgwIEYO3ZsVENS/slcF5G4qA+0vyxGdrORsLd7Py4KvpcRZPBXaIGsDpNxfvBu2NtMgKjQwLTxFSTPbADrsgehPrYGCAelTkpU5On2zgSCPrgaDZU6Sp44056GKFfDuHm81FGomFNc2A/N8e/hajAYokofm0kEAfbWb0AIBWHaMDo2cxARERERESEfRd+tW7fi+eefR3JyMsaOHYvevXtfuq9t27aoXr065s6diyVLlkQ1KEVOcfEQEud3gjL9IGydZsCVNjzuWyaIajPcN92LjP7LcfGedXDVfxDKM9uQsHQQkmc2hnHTOMgzf5M6JlGRJPid0O/9DL4qHRBMqCZ1nDwJ60vB1WgotL8vh/LsDqnjUDFm2P4+wioTXDFuuxCyVIKjyVPQ/r4c6mOrYzoXERERERGVXBEXfadOnYqEhAQsWLAAffr0QZkyZS7dV79+fXz55ZcoU6YM5syZE9WgFBn1sdVI/KorBDGM9D5L4K3WWepIEQsm1oCjxcu48OAuZHb5BIEy9aHfNQ3Js1oicUFXKM/tkToiUZGiO/AlZL4sOBs/JnWUiLgaPoyQLhmmja8Coih1HCqGFBm/QPv7crjqPwBRbYr5fK6GDyOQWAOm9aMg+F0xn4+IiIiIiEqeiIu++/fvR4cOHWA2m3O932Aw4Pbbb8fx48cLHI7yQRSh3zkV1qX3I2ithvR+yxFMrid1qoKRK+Gr0gG2Oz/DhQd3IvvWlyB3nEHiV92g2/sJi0BEeRH0Qb9rGnzlmyNQpqHUaSIiKnVwNHsOqrPboT62Uuo4VAwZtr+PsFIHV/0HC2dCuQr2NhOgcJyGYetbhTMnERERERGVKBEXfcPh8HWP8fv9CAaj03/Vbrfj2WefRVpaGlq0aIG33noLoVAIAHD69Gk88MADqF+/Pjp27IgNGzZEZc4iK+SH+ftnYNr0KrzVuiCj9yKEDWWuf14REtYnw9XoUVy8ew18FVrCvP5FWFY+CsHvlDoaUVzT/rIYcte5IrfK92+e2v0QSKgG06bXgFBA6jhUjMhtx6D59Ru46w2CqC28jUMDZdPgqnsP9Ls/huLC/kKbl4iIiIiISoaIi741atTA+vXr4ff7c73f6XRiw4YNqFmzZoHDAcCYMWNw/vx5zJ49GxMnTsSSJUvw6aefQhRFDB06FBaLBQsXLkSPHj0wfPhwnDx5MirzFjWCJwMJX/eH7tA8ONKeQlbHqYBSJ3WsmBE1Vti6fobsW16A5rdlSJrbAYr0w1LHIopP4RAMOz9EoFRd+Cu0lDpN/sgUcDQfBUXWMegOzpU6DRUjhh0fADIVXA0eLvS5Hc3/h7DGCvMPzwPhUKHPT0RERERExVfERd9Bgwbh1KlTGDJkCA4ePHip+BsOh7F//34MGTIE58+fx4ABA6IScMOGDRg0aBCqV6+Opk2bokuXLtiyZQu2bNmC48ePY+zYsahatSqGDBmCBg0aYOHChVGZtyhRZPyKpPldoDq3G7YOU+Bs9hwgRPyjLXoEGVypw5DZ8ysIfieS5neG9tACqVMRxR3NsVVQ2I7C2fjxuN/M8Vp8N94BX9kmMGx5i6v7KSrk2SehPbII7pvuRlifXOjzixoLsluOger8Huj2f17o8xMRERERUfEVcWWwY8eOePTRR7F161b07t0bM2bMAADUq1cPffv2xa5du3DPPffgzjvvjEpAi8WCb7/9Fh6PB+fPn8fGjRtRp04d7N27F7Vr14bBYLh0bKNGjbBnT8na3Ev153okLrgTQsCNjN6L4K3RQ+pIhc5fvhnSB6yGv0wjWNY8CfP3zwBBj9SxiOKDKEK/YzKC5krwVi16GzpeRhDgaPES5J506Hd9JHUaKgb0O6YAggzOho9KlsFbvTt8FVrCuHk8ZM6zkuUgIiIiIqLiRZGfk5544gm0bt0aCxcuxKFDh+BwOKDT6VCjRg306NEDTZo0iVrAl19+GSNGjEDDhg0RDofRtGlTDBs2DG+88QaSky9flZOYmIhz585Fbe54p9v7CUwbRiOYWBOZd36GsKm81JEkE9YnI7PHPBi2vAXj9klQnt8LW6fpCFkrSx2NSFKqUz9BdX4Pstq8CcjkUscpsECZhvBUuxP6XR/BfdO9COtLSx2JiiiZ8yx0h+bBXasfwsay0gURBNhbv4FSs9vAtGE0sjrPkC4LxQdRhH7nFOgOzkNYZUBYmwBRY0VYm4CwxoqwJgFh7ZWfodBKnZyIiIiI4ki+ir5AzsreevXqRTNLrk6cOIHatWvjscceg9PpxKuvvooJEybA4/FAqVRedqxKpUIgcO0NfuRyORITC2+jlrxSKBR5zxUKQP7dM5Bvm4ZwzS4Qe30Oq9pw/fNKgjvfRKBGGygW3Y9S8zsi2H0GxDolb/VzbiK6xiguKZVKCIIQ0c9RsWwaREMZ6Jo/DJ1SE8N0hajTeAgf3IykPVMQ6jpF6jTFSkl6nJBvfR0QQ1DeMQqJVom/58REhFqPgvb7l6C4+DPEml3yPdTfz43i8edYkq6vfAt4If/2Ucj3zkG4YgvIVDrAnQnhwp+AJxOC137VU0WlDtAlQtQlAtoEiPokQJsA6JIg6nL/DKWuSLf9+TdeX0RERESXy3fRVxRF7NixA0eOHIHb7YbZbEbdunVRt27dqIU7ceIEXn/9descJkEAACAASURBVPzwww8oU6YMAECtVuOBBx5Anz594HRe3tPR7/dDo7l2USMUCiEjIyNqGaMlMTExT7kEbxasKx6G/ORGOBsNhaP5/wCnL+eDciSmQtZ/JazfPQLV/P5w1X8I2be+CMhVUieTVF6vMYpfgUAASqUyzz9Hxfl9KHV0LbKbj4Ir2wXAFduAhcYK000Dodv5KTJr3YtgQjWpAxUbJeVxQua6iOQdH8NToxfsYRMQD99zzXuRtHs2hKXDkWGuB1Glz9cwf7/4HY8/x5JyfeWXzJ0O67IHID+7A45mI+BMfeLKgmzID5k3CzKvDTJPJmReG4S/Psv+/dltgyzjeM7XvqyrzinK1ddcQSz+/fW/VhqLKkNcFop5fUUmJSVF6ghEREQUY/kq+m7YsAFjxozB2bNnIYripdsFQUC1atUwfvx41K5du8DhDhw4AL1ef6ngCwB169ZFKBRCqVKl8Ouvv152fHp6OkqVKlXgeeOV3HYMCUsHQW4/gaw73oWndj+pI8WtsKk8MnovhmnTOOj3fAzlud2wdZxaoltgxBuZ4wwSF/eFr8JtyG7xMqBQSx2p2DHsnIKwygT3TQOljhJ1zrSnoD28AMafXoftzk+ljlOkyZxnoT28ELrDCyBLqga0/6jYv0im3z0dCPrgTB0mdZR/yFWwt5mApK+6w7D1LThavCx1IipEivTDsH47CHJPOmydpsFb7Sp7Y8hVCOuTI9t4MByEzGuHzJt5ZYHYmwmZ55/PyvRDELw2yDw2CBBzHU6UKS8rAl8qBv/n639/FlWmuCwUExERERVnERd9t23bhqFDh0KlUqFv376oV68e9Ho9Lly4gN27d2PVqlUYOHAg5s2bh6pVqxYoXHJyMrKzs3H27NlLr0YfPXoUAFC5cmXMnDkTbrcbOp0OALBz507Ur1+/QHPGK9XJn2BdPhgQBGT0XIBAuej1TS625CpktxwLf9lUmL9/BqXmtkdW+w/gq9RG6mQU8uesWHechn7fZ1Ce24WsTtMRMleQOlmxIbcdg+a3ZXA1fhyi2ih1nKgL6xLhbPQ4TD+Ph/L0Vj4mRirog+b4amgPzof6xHoIYhj+5Juh+G0lzPJnYW83qdgWaARPJnT7PoO3eleErFWkjnOZQNk0uOreA/3uj+Gp0RPB5JukjkSFQH1sDSwrh0JUGZHR+2sESt8c3QlkCoR1iQjrImh9IIYh+OyXrx7++/O/CsaCxwZF5q9/FY5tEMRQ7sMJ8r8KwFdfQXxFEVljAYSI95wmIiIior9EXPSdPHkyVCoV5s6di5o1a15238CBA7Fjxw7cf//9ePvttzF16tQChatfvz5q1aqFF154ASNHjoTX68Xo0aPRrVs3tG/fHlOmTMHIkSMxbNgwrFu3Dnv37sVrr71WoDnjkfbAlzCvewFBS2XYun6OkLmi1JGKFG+1OxFIqg3rioeR8M09cKQ9CWeTZ4rFplZFlWnjWKjO7YSt03SIMgUsq59E0tz2yGr3HnyV20sdr1gw7JoKyFVw1X9I6igx42rwEPT7PoNp06vI6Lu02BYpo0lx8QB0h+ZDe2QxZF4bQoYUOFOHw1OrL0KWSii1fzp0P7yCkKk8nM1GSB03JvR7ZkIWcOW8dT4OOZr/D5qjK2H+4fmc65q/q4ovUYR+93QYN45FMLluzqa8hjh5y70gg6ixIqSxIvcybi7EMAS/I/dC8X/aUMiz/oDSsyvn9nDu+3GIggyi2nzVovDlbSgSAL0y13GIiIiISqqIi7779+9Hhw4drij4/q1x48Zo37491q1bV/BwCgWmTZuG119/HYMGDYJSqUSHDh3w7LPPQi6X48MPP8SoUaPQs2dPVKhQAZMnT0b58sXo7fvhEIwbx8KwZwa8FVsjq+NUiGqT1KmKpJC1CtL7LYV53SgYt70H1ZkdyOowBWF98W0HEq80vyyBfu8ncDYYAm+1nM2K0gesgnXFECQsvR/ORo/C0WwkIOcfb/klc56D9vBXcNe+q3hf40odHM2eg+X7Z6D5ffml64kuJ3gyof3la+gOzYfy4gGIchW8VTrAXbs//De0uKyoGG45Eu7zv8C47T2EjOXhqTtAwuTRJ/iyod8zE94qHRFMyv15jNREjQXZt70C66rHodv/Odw3PyB1JIqFkB/mdS9Ad3AuPFU7w95uUs5GbEXZX0XakNqMEG7M2zmiCMHvvKLlhOC5svWEPPsUlOf35RSKQ1fuZSHKlTA2fBSO1OE5G9QRERERlXARF321Wi30+mtvLpKUlASFIt97xF2mdOnSmDRpUq73VaxYEbNnz47KPPFG8DlgWTkUmj/W5mxE1mI0IIvO/9MSS6GF/Y534C/XBOZ1LyBpbjtkdZwKf7mmUicrMRQZv8K89ln4U1LhaD7q0u0hc0Wk9/kGph9fgWHnVKjO7szpwRwvK56KGP2eGUA4CFejR6SOEnOeWn1zVsr99Aa8ldsV+160eRYOQX3iR2gPzYPm2CoIIT8CyTfB3uo1eGp0h6ix5n6eIMDeejxkzrMw//A8Qsay8FdsVajRY0m39zPI/NlwpMXnKt+/eWv0gO/wAhg3j4e3Skc+FhYzgicT1uWDoT79MxypT8DZ7LmS28ZAECCqjQipjXlv8SSKEALuf60ezmk5YTq3GYbt70NzZDGyW42Dr3K72GYnIiIiinMRP8Ps0qULli1bhrNnz+Z6v81mw4oVK9C+Pd+inV9y+wkkftUV6j/Xw95mArJbjmXBN4o8tfshvd9yiEo9Ehb1gX7HZEAMSx2r2BP8TliXPwRRoYOt00dXruRVaJDdZjxs7SdDcfEAkua0g+rEj9KELcIEbxZ0+76At1rXktEKRiaHo/mLUNiPQ3egeL4IGAm57RiMm8cj+dM0JHxzN9QnN8F900BcHLAG6Xetgvvm+69e8L00iBJZnaYjmFQT1uWDobh4oHDCx5jgd8Gwexq8ldoimFxP6jjX9lfxXQgFYdowWuo0FEXyzN+QNL8LVOd2wdZ+Mpy3PF9yC775JQgQVXqETOURLF0P/oqt4K3ZE6FenyKj1yKISh0Slt4H69L7IM8+KXVaIiIiIslct5K4du3ay76uX78+VqxYge7du2PQoEFo0KABkpKSkJ2djf3792PWrFnQarXo1q1bzEIXZ8oz22Bd9gCEcAiZ3efAX6GF1JGKpWBSLaT3/w7mtc/C9NPrUJ3Zhqx2k65fDKH8EUWY1z4LedYxZPaYf81Va96aOZsXWZYPRsLXd8HZ9Bk4057kH8V5pNv3RU6/0saPSR2l0PgqtYGvfHMYtr4DT80+xXLjumsR/C5oflsK7aH5UJ/ZClGQwVexNbJvGwtv5TvytfpZVBmQ2XUWkuZ3QcI39yK971KETUW7fZLuwCzIvDY443yV799ClkpwpD0J08/j4Tm2mqsWiwHVn+thXfEIRLkKGb2+QiClsdSRih1/+WZIH7AG+j0zYNjyNkrNaglH2lNwNXyY7wQhIiKiEkcQRVG81gE1a9aE8J/Ncf59ytXuEwQBhw8fjlbOqPH7/cjIyJA6xhUSExPh3vQRzGufQ8hYHpldP4+7XcWLJVGEbt+nMP04BiF9aWR1mo5AmfpSp4qJxMREya593Z6PYd4wGtm3vABX6rA8nSME3DD98Dx0RxbBV6ElstpPjmzn8WKoV69eUCqVmDdvXu4HBD1I/iQNgeSbYetesla9Ks/vRdK8jjlvlb7leanjxJ4oQnlmG3SH5kHz21LIAm4ELZXhrt0Pnlp9EDaUydew/32cUKQfQeJX3RAylkVGnyUQ1eZofQeFK+hB8qfNEEysjsyeC6ROk3chP5LmtIMQcCH9nvUQVddurwXkPE4AwKJFi2KdLmJS/h6Smm7vpzBtGI1gQvWcTXmL+Iso8ei/15cs+xRMP74C7dEVCFqrwt76dfhvuFXChPElJYVtY4iIiIq76670feyxx64o7FKUiSLka16EZeNE+MrfClvnaVxxWlgEAe6bH0CgdANYVgxB4sLuyG7xMtz17gN43UeF8uwOmDaOhbdyO7giWH0qKnWwt3sf/rJNYN7wEpLmtoOt40cIlE2NYdqiTXdwPuSeDGSlPi51lEIXKH0zPNW7w7B7Gtz1BhbbHqgy51loDy+E7tB8KLKOIazUw1u9G9y1+yGQkhr1x61gUk3YusxEwpK7YV02GJndZxfJ1XK6g3Mhd19AVscPpY4SGbkK9jYTkLSwBwxb34KjxctSJ6JIhXNadOj3fQbvjbcjq8OHEFUGqVOVCGFTeWR1+Rie42th2vAiEhf3hadGD2S3GI2wvrTU8YiIiIhi7rpF32HD8rYqj/JP5jgN+caJcNW9B9mtXruy1ynFXKBMA6TftQqW1U/AvH4UVGe2wd52Iv8wKyCZOx3WFQ8jZCyHrHaTIm/RIAjw3HQPAqXrwbriYSQu6gVH81FwNRjCovx/hYPQ75oKf0oj+Ms2kTqNJBy3jITm9+UwbnkL9tvfljpO9AR90BxfDe3B+VCfWA9BDMNXrimcqcPhrdo5T6s/C8J/w62w3/42LKuHw/z9s7C3m1S0/v2F/DDsmAJ/2TT4yzWTOk3EAuWawF33buh3fwxPjZz2N1Q0CD47rCsehvrEj3A2fCRnA1OZXOpYJY7vxra4eMMtMGyfDMPOKVAf/x6OZiPgrjeIe2YQERFRscYmmXEgbCoP/4iTyG4zgQVfCYnaBNi6fo7sW16A5relSJrXEYr0I1LHKrrCIVhWDoXMY4Ot84wCvS08mFwP6XethO/G22HaOAbW5Q9B8NmjGLbo0/z6LRTZJ+Fs/HjRKshFUchcAa6b74f20HwoMn6ROk6BKS4egGnDSyg9syGsKx6GMuMwnI2H4cKgn5DZezE8tfvGvOD7N0+t3nA0GwHdkYUwbJlYKHNGi/bwV5A7z8KR9mSR/beR3fx/CGusMP/wPBAOSR2H8kCedRxJ8++E6tTPyLr9bThajGbBV0oKLZzNnsPFu39AoEyDnHcQzesE5bldUicjIiIiipmIX94Oh8P48ssvsWzZMpw+fRp+vz/X4wRBwNatWwscsMQwJAO+ktnnLq4IMrhShyGQ0giW7x5F0vxOsLceD0/tvlInK3IMW96C+uQmZN3+DoKl6hZ4PFFthq3zTOh3T4fxp9eQNLcDbJ2mc9UbAIgiDDumIJBQHb4b75A6jaScqcOhOzgPxp9eg63rF1LHiZjgyYT2l6+hOzQfyosHIMpV8FZuD3edu+C/oYWkRSNn6hOQZ5+Ecdt7CBnLw1N3gGRZ8iwchGH7B/CXrg9/hZZSp8k3UWNF9m2vwLrqcej2fw73zQ9IHYmuQXVqM6zLBwMAMnvMg7980VthXlyFrJWR2X0uNL8vg2nDy0icfyfcde+Go/kLbK1GRERExU7EK30//PBDvP7669i7dy8AwGAw5Pqh1xfO6iOiWPCXvwXpA9bAX6YhLGuehPn7Z4CgR+pYRYb62BoYt0+Cu85d8NTpH72BBQGuhg8jo9dCCCE/khZ0hXb/bODa+1EWe+o/foAy43BOz+RIW2gUM6I2Ac7UYdAc/x6qU5uljpM34RDUf6yDZcXDKD2zIcwbXgIEAfZW43D+od3I6jQN/oqtpF8lKAiwtx4Pb8VWMP/wPFR/rpc2Tx5of/kaiuwTcKY9UWRX+f7NW6MHfBVug3HzeMicZ6WOQ1ehPTAHCV/3R1ibiPR+y1nwjUeCAG+1O3Hx3g1wNRgM3cG5KPVFC2gPzgPEsNTpiIiIiKJGEMXIqiVt27aFKIqYNWsWypUrF6tcMeP3++Ny5+iSvKN1XAsHYdzyFgzb30cgqTZsnaYjZK0sdap8KaxrTG4/gaS57REy3YD0vt8ACm1M5pG5M2BZ9TjUJzbAXbM3stuMh6jUxWSueNGrVy8olUrMmzfvstsTvuoBRfZJXLjvZ7aIAYCgB8mft0BIVwoZ/ZfHbSFcnnUcukPzL7UfCGus8NTsCXftflFZHZ8feXmcEPxOJC7sAXnWH8jo87VkWa8rHEKp2a0gytVIH7CmyBd9AUCe9QdKzW4D741tkdV5Rq7H9OrVCwCwaNGiwoyWJ8X6uU44BONP42DYNQ2+Ci1h6zQNotokdaoSJb/Xl+LiQZjXvQDV2R3wl02DvfUbCCbVikHC+JKSUjw3PCUiIqJ/RNzeITMzEwMGDCiSBV+iiMkUcNwyEv6UVFhWD0fSvA6w3/4uvNU6S50sPgW9sC5/CABg6zwjZgVfAAjrEpHZbTYM2yfBsOVtKC/uR1an6QgmVIvZnPFIeWYb1Ge2wt5yLAu+f1No4bjleVhWPwHTDyMRNpSROtHlxDBUJ3+C+sxWiIIMvoqtkX3bGHhvvANQqKVOd12iyoDMrrOQNL8LEr65F+l9lyJsKi91rCtofl8Ohe0obJ2mFYuCLwCELJXgSHsSpp/Hw3NsNXyV20kdiZDzQohl5VBojn8P180PIPu2V7hBWBESLFUHGX2WQHtoPkybxiFpTju4GjwEZ5NnuKEvERERFWkRPyOtXbs2Tpw4EYssRHHLd2NbpN+1CpYVD8O6YjCc9QfDcesoQK6SOlpcMa9/EcqLB5B55+cImSvGfkKZHM4mT8NfphEsqx5H4ryOsLedCG+NHrGfO04YdkzJWSFapwj0Vy1Enho9oT28EPoDs6WOkqugpTKyb3kBnlq9ETYUvdVWYUMZZHabjcSvuiHh23uR0WdJgTZrjDoxDMO2SQhaq8JbpZPUaaLK1egRaH9ZDNP6UUi/4dZi/w6HeCfPPgXrt4OgyPwV9tZvwF1vkNSRKD8EGTx17oK3cnuYNr8Bw65p0P76bc4LclU7F5sXjoiIiKhkibjo+/TTT+P+++/H3Llz0b9/fwh8EkQlRMhUHhl9voZp41gY9syA6twu2Dp9hLCRq94BQHtwHnQH58CZOhy+yoW7mZi/YkukD1gN64pHYF35GFynt+WstCoCqyYLQpF+BJrja+Bo+iwLP/8lkyOzxzwAcdrvOU5bTkQimFQTti4zkbDkbliXDUZm99lx80KY+thqKDMOI6v9B9L3Qo42uQr2NhOQtLAHDFvegqPFaKkTlVjKM9thXfYghJAfmd1mw1+x6G4WSDlEbQLsbSfCXbs/zOtGwrpiCLwVWyG71WsIWW6UOh4RERFRRCIu+jZq1Aj9+vXD2LFjMXHiRKSkpECluvKPPEEQsHjx4qiEJIobchWyW42Dv1wTmNc8jVJz2iGr/WT4KrWWOpmkFBcPwLzuf/CVvxWOps9JkiFsSEFGr4Uwbn4Dhl0fQXl+N7I6TUfIXEGSPIVBv/NDhJU6uOrdJ3WU+CQIAPjCZCz5b7gV9tvfhmX1cJjXPgf7He9JvyJOFGHY9h6C5krwVO8mbZYYCZRrAnfdu6HfPQOemj3jt69yMaY9sgjm759ByFAOGV0/Q6iEtRYq7gIpjZDe/zvo9n0G489votTsNnA2fgzOxo8DCo3U8YiIiIjyJOKlRp999hlmz54NURThdrtx9OhRHD58ONcPouLKW+1OpN+1EiF9GVi/uQeGn98EwiGpY0lC8NlhXT4EYY0FWR0/lHZVnVwJR4vRyOzyCRRZfyBpbnuoj62SLk8MybNPQfvL13DXvQeiNkHqOFSCeWr1hqPpc9Ad/gqGLW9JHQfqP9dDdWFfTnGmGPdVzW7+P4Q1VpjXPl9if/9IQgzDuHk8LKuGwZ/SGOn9lrLgW1zJFHDXfwgXB26Et0pHGLe+g1Kz20D9xzqpkxERERHlScRF3y+++AIWiwUzZ87E7t27ceTIkVw/WPSl4i5krYL0fkvhqd0Xxm3vIeHruyBzXZQ6VuESw7CsfgJyxynYOk1DWJckdSIAgK9KB6QPWIWQuQISlt4P46ZXgVBA6lhRpd/1ESDI4GowROooRHCmPQl37f4wbnsX2oNzpQsiijBsexchQ1l4avWWLkchEDVWZN/2ClTnd0O3/wup45QIQsANy/IhMGx/H+66dyOz+xy+6FYChPWlkdXxQ2T0mA/I5Ej45m5Ylg+GzHFa6mhERERE1xRx0TcjIwNdu3ZF8+bNodVqY5GJqOhQ6mC/411k3f42VGd3IGluO6hOb5E6VaHR7/wQmmOrkX3raATKpkod5zIhc0Wk9/kGrpsGwrBzKhIX94HMeVbqWFEhc2dAd3AOPDV7ImwsK3UcIkAQYG8zAb4KLWFeOwKqP9dLEkN1ajNUZ3fA2fixuOkvHEveGj3gq3AbjJvfgMx5Tuo4lwsHIbcdg/rYGuh3fQTz2ucgX/wgdPs+h+LiwSK3OlnmPIvEhT2gObYS9tvGwN7mzRJxjdE//BVa4OKA75HdbCQ0x9ei1KyW0O+cWuxeVCYiIqLiI+L3PVapUgU2my0WWYiKLE+duxBIvhnWFUOQsKgPHLc8D1ejocVis6arUZ3aDOPm8fBUuxPu+g9KHSd3Cg2y24yHv2wazD+MQNKcdsjqMAX+CrdJnaxAdHtnAkFfzjVGFC/kStg6TUfiwh6wLh+MjD5fF3qvWcO2SQjpkuGuc1ehzisZQYC99XiUmt0Gpg2jkdV5euFH8GRAYTt6xYfc/ieE8D/FsJA2ATK5EmbnbABAWGVEoExD+FMaw182FYEyDSGqDIWePy+U5/fCuvQ+CAEXbHd+Bt+Nt0sdiaSiUMOVNhzeGt1h2vAiTJtehfbwAthbj0egXBOp0xERERFdJuKi76OPPopnnnkGHTp0QJs2bWKRiahICpaqjfT+38H8/TMw/fQ6VGe2IavdJIgaq9TRok7mPAfLikcQslSG/fa3pd+46Tq8NXsimHwTLMsHI+Hru+Bs+gycaU8WyaK8VhaEfu9n8FVpjyD7SFKcEdVGZHabhaT5XZDwzUCk91uKsLFcocytPLMd6lObkN3i5RK10VLIUgmOtCdh+nk8PMfWxGaSoA8K+5+Q245CYfs9p7CbdRQK2zHIvP8sBBDlKgTNlRBMqAZvlQ4IWqtc+hA1ViQmJCDr+B6ozm6H8swOqM5uh2HrOxAgQhRkCCbVyikCp6QiUDYVIWN5yX+/aH5bCsuqJxDSl0Jm97kIJtWUNA/Fh5C5Amxdv4D62CqY1r+IpIU94K7VF45bX4ybVldEREREgiiKYiQnfPTRR1i1ahWOHDmCcuXKoWLFirm2eRAEAR988EHUgkaL3+9HRkaG1DGukJiYGJe5KB9EEbq9M2Ha+CpC+tLI6jwDgdI3S50qetdYKIDExX2guHgAGf1WIJhYveBjFhIh4Ibph+ehO7IIvgotkdV+MsK6RKlj5VmvXr1wd4XzeKzSMaT3XYpASiOpI1ExE63HCUX6YSR+1R0hYzlk9FkCUW2KQrprsy65B8oLe3Dx/m0QlbqYzxdXQn4kzWkHIeBCu+/KwRuWY9GiRZGNIYqQuS/8s1L336t2s09AEMP/TKdLRjChKoKWKgj9q7AbMpW/5uZ5uV1fgs8B5bmdUJ3dAdWZ7VCe2wVZwJUzj77MPyuBUxojUKouIFdG9n3llyjCsO09GLdMhD8lFbYuM1nMi3NSPZcWAm4Ytk2CftdHEJU6OG55Ae66d0u7sW0epKSkSB2BiIiIYiziom/Nmnlb4SAIQlxu5saiLxUW5bldsC4fApknHdktXoG73iBJVyxF6xoz/jgGht3TYOswBd4aPaKQrJCJIrQHvoR5w0sIaxNg6/hR3PUjvpr+vXtgYZPdMFVujMxeC6WOQ8VQNH8XqU5sRMI3d8Nfrikyu82Oaf9T5fm9SJrXEdm3vABX6rCYzRPPlKe3ImlhD3x5MgVTjle6etE34IYi6/jlrRhsR6HIOgaZ33HpMFGhQdBS+Z+CrrUKgpa/Vu2qjfnKmKfrKxyCIuMwVH+tBFae2Q6F49SlTP7S9RFISf2rGNw4Nu+mCXpgWfMMtL8ugbtWn5z+vQp19OehqJL6ubQi8zeY1v0P6lM/wV+6Aeyt30CwdD3J8lwPi75ERETFX8RF39On875TbblyhfOWzkiw6EuFSfBkwrJqODR//gBP9W6wt50oWc/CaFxjmt+WwbpiCFw334/sVq9FKZk0FBf2wbriYcgdp+FoPgquBkMkfxvx9XwytAVG1TiKjO5z4K/YSuo4VAxF+3eR9vBXsKx+Iqdwdsd7Mfs3Zl32AFSnfsaF+7fluyBZHJjXPgfV/i/x0K56mDD543+1YfhXcddx+fO4kKHsZW0Ycgq8VREylo16C5z8Xl8y51mozu641BJCefEAhHAQABC0VoW/bOqlFcEhS5UCXWcy14Wc6+ncrpwXERo/Hve/GyhHXDyXFkVofvkapo1jIPNkwF1vEBzNRkBUm6XNlQsWfYmIiIq/iIu+RR2LvlToxDD02yfDuOVNhCw3wtZphiQ9AQt6jcltvyNpXicEE6ojo/fiYrFrueCzw7LmKWiOroS3Skdk3fFO4f9hFnBD5rVB5sm89mdvJvxnDuKMTwvDC7+yCEExEYvfRYat78K4ZSIcaU/B2ey5qI4N5LSSKPVlWziaPA1n02ejPn5RInhtUH1QHwmqwGW3h5X6f1br/ru4a6lcqK0wonZ9BdxQnd8L5V8tIVRnd0DmywIAhDXWv/oCN4a/bBoCpesBiivbkOVGcfEgEr4dBMFrQ1b7yfBV7VjwrFRo4um5tOCzw/jzROj2fYawvgwuDPwRiLO2Myz6EhERFX8Rb+TmdDrzfKzBEJ+7MBMVKkEGV9pwBFIawbJyKJLmd4K9zQR4avWROlmeCQE3rMsHQ5SrYOs0rVgUfAFAVJth6zwT+t3TYdw0DklzO8DWaTqCyTflYzARQsANmTcTgienSJtTsM2E7O+vcynmCkHvVYcMq0wIaxMQ1lgR0iVjQ8Y5fHXuBrzOgi8VIc60JyHPPgnjtncRMpWHp85dUR3fsH0Swko9XPUfjOq4RZGoseKlw9Vwa6INne594lJxN6wvXbxeKFLq4C/fDP7yzeACADEMFX0Q8QAAIABJREFUhe0olGe2Q3U2pwisOZ6zqZ0oUyKQfBP8KY1z2kKUbZzz/+M/1MdWwbLyMYhqMzL6LMnf7wGiv4hqM7JbjYO7dl+oT/xYojaXJCIiovgRcdG3cePGEPL4h0M89vQlkor/huZIH7Aalu+GwrL6CahOb4W91at5XoEkGVGEee0IKDJ+RWb3OQgb469tS4EIAlwNH4a/TANYv3sUSQu6wt7yVXird72iSCtcZyWuEPJfdZqwxprzobUiZEhBIKkOwlorwpqEvwq7CRD//lpjRVhjuWLDonFf9IJSWUibGBFFiyDA3mYC5M6zMK8dgZAhJWrtSeS236H5dSlcjR+LTW/XImi33YzddjNa1RsodZTCI8gQTKiGYEI1eOoOyLnJk/HX5nA5LSH0+z6HsHs6ACBoqoDAX+0g/CmNof5zPYw/vY5A6fqw3flJrkVhovwIJtdDMDl++/oSERFR8RZx0Tc1NfcNj7xeL06ePImsrCzUr18f9erxCQ7Rf4X1pZHZcz6MP0+EYccHUF7YC1un6QhZbpQ62lXp9n8B7S+L4Wg2Av6KLaWOEzOBsmlIv2s1LKseg+WHEcAPI3I9ThRkCGssEDU5BdqQ+QYESt/8nwKu9bLPotoS97t4E8WUXAlbp+lIXNgD1uWDkdHnawRL1S3wsIbtHwAKdU5PbqJ/EbWJ8FVuD1/l9jk3hPxQXtj/V2/g7VCd3AjtL4svHe+p3hVZd7wb/y/EEhERERHlUcRF31mzZl3z/i+//BJvvvkmRo4cme9QRMWaTAFH8xfgL5sKy6rhSJrbAfY73oW3aiepk11BeW43TBtGw1upLZypw6WOE3NhXSIyu30J7eGvIPParlLANUd9cyOikkBUG5HZbRaS5ndBwjcDkd5vaYHeOSC3/wntkcVw1X8QYV1SFJNSsSRXIZDSCIGURkDDhwFRhDz7BFRnd0CEAG+NHsWrBQYRERERlXhRr1zcfffdaNq0Kd55551oD01UrPhuvB3pA1YjaK0K6/KHYNzwMnCN9gCFTfBkwLJiCEL60shq/37JKXTK5PDU6Q9Xo0fhqd0PvsrtEEhpjJC1Ss7bx0vK/weiGAgbUpDZbTaEgAsJ39wLwZed77EMOyYDMjlcDR+JYkIqMQQBIXNFeGr2grdmTxZ8iYiIiKjYiUn1onr16ti/f38shiYqVkKm8sjo8zVcNz8Aw54ZSFzUCzLHaaljAeEQrCsfh9x9EbbOH7NXJhFFTTCpFmydP4bC9jusyx/K14tdMsdpaA8tgLv2XQgbysQgJRERERERUdEW9aJvOBzG9u3bodFwl1qiPJGrkN1qHGwdP4Ii/QhKzWkH9R/rJI1k2PYu1Cc2wN5yHIKl2Z+biKLLX6EF7Le/DfXJTTCvfQ4QxYjON+ycCkCEq/FjsQlIRERERERUxEXc0/eLL77I9XZRFOF2u/Hjjz9i79696N69e4HDEZUk3updEShVB9blQ2D95h44056As8kzhb4BmPqPdTBsfRfuWn3hqXt3oc5NRCWHp1YfyLNPwbhlIkLG8nA2ey5P58lcF6A7MAeemr0RMpWPcUoiIiIiIqKiKeKi7+uvvw5BECBeY1VOnTp18OyzzxYoGFFJFLJWQXq/pTCvHwXjtvegOrsDWe2nIKwvVSjzy7NPwbLqcQSTasHe+nX2OCSimHKmPQl59kkYt72LkKk8PHXuuu45+l0fAWE/nKnDCiEhERERERFR0ZTvou9/CYIApVKJypUro1atWlEJR1QiKXWw3/Eu/GXTYF43Cklz28HW8SMEyjWJ7bxBHywrhgDhIGydZwBKXWznIyISBNjbTIDceRbmtSMQMqTAX7HV1Q/3ZEC3/wt4q3dHyHJj4eUkIiIiIiIqYiIu+vbs2TMWOYjoPzx17kIguR6sKx5G4qLecNwyEq5GjwJCTPZfhOnHV6A6vweZXWaymEJEhUeuhK3TdCQu7AHr8sHI6PM1gqXq5nqofvfHEAIeOFOHF3JIIiIiIiKiouW6Rd+BAwfma2BBEPD555/n61wiyhEsVQfp/b+D+ftnYPrpNajObENWu0kQNZaozqM9sgj6/Z/D2ehR+Kp0jOrYRETXI6qNyOw2C0nzuyDhm4FI77cUYWO5y44RfHbo934Cb9XOCCZWlygpERERERFR0XDdou+2bdsiGvDvfr+5tYAgosiJaiOyOk2Df+9MmDa+iqS57ZHVaToCpW+OyviK9MMwr30OvnJN4bjlhaiMSUQUqbAhBZndZiPxq+5I+OZeZPRZAlFtunS/fs8nkPkdcKZxlS8REREREdH1XLfou3379jwNdPz4cbzyyis4dOgQlEolhgwZUuBwRPQXQYC7/kMIlGkI6/IhSPyqG7JbvAJ3vUEF2mxN8DlgXT4YYZUJWR0/AmQRd3whIoqaYFIt2Dp/jIRv7oZ1+UPI7DYbkKsg+J3Q7/kY3hvvuGrrByIiIiIiIvrHdZuDGo3Ga37odDrMmTMH9957Lw4dOoQGDRpgyZIlGDaMu2oTRVugTENcHLAavvK3wrz+f7CsHArB78zfYKII85qnILf/CVunaQjrk6MblogoH/wVWsDe9i2oT26Cee1zgChCt+9zyLw2ONOelDoeERERERFRkVCgZX379u3Diy++iN9++w16vR7PP/88BgwYEK1sRJQLUZsAW7cvoN8+GcYtb0J58QBsnWYgmFQzonH0u6dDe3QFsluMRqBckxilJSKKnKd2X8gdp2Dc8hbCulLQHloAX4WWCJRpIHU0IiIiIiKiIiFfRV+32413330Xc+bMQSgUQtu2bTF69GiULl062vmIKDeCDK604QikNIJl5VAkze8Ee5sJ8NTqk6fTlae3wrhpHDxVOsHV4OEYhyUiipwz7SnIs0/CsPNDAICNq3yJiIiIiIjyLOKi74YNGzBmzBicOXMGpUqVwksvvYR27drFIhsRXYf/huZIH7Aalu+GwrL6CahOb4W91auAQnvVc2SuC7B+9whC5oqw3/FOgXoCExHFjCDA3uZNCH4XAJHvSCAiIiIiIopAnou+mZmZGDduHL777jsAQN++fTFixAgYDIaYhSOi6wvrSyOz53wYf54Iw44PoLywF7ZO0xGy3JjLwUFYvnsUMp8d6d3nQFSbCj8wEVFeyZXI6jxd6hRERERERERFznU3cgOARYsWoWPHjlixYgUqVaqEWbNmYezYsSz4EsULmQKO5i8gs+sXkGefRtLcDtD8vuKKw4ybx0N9+mfY27yJYFItCYISEREREREREVGsXXel76BBg7Bt2zYAQO3atTF48GDY7XasXbv2uoO3bdu24AmJKM98N96e0+5hxcOwLn8IzvqD4bh1FCBXQTj8DQw7P4TrpoHw1OotdVQiIiIiIiIiIoqR6xZ9t27deum/Dx06hKeffvq6g4qiCEEQcPjw4YKlI6KIhUzlkdHna5g2joVhzwyozu+Co9lIKJY/BH/yzci+bYzUEYmIiIiIiIiIKIauW/R9/PHHCyMHEUWTXIXsVuPgL5sG8/fPIHFxH4jaBGR1ngEo1FKnIyIiIiIiIiKiGGLRl6gY81bvikCpOjBtGgf5bU8hZCovdSQiIiIiIiIiIoqxPG3kJpVAIIA33ngDTZo0QZMmTfDyyy/D7/cDAE6fPo0HHngA9evXR8eOHbFhwwaJ0xLFp5C1Cmx3fgqxcmupoxARERERERERUSGI66Lvm2++iTVr1uDDDz/E1KlTsXHjRkyZMgWiKGLo0KGwWCxYuHAhevTogeHDh+PkyZNSRyYiIiIiIiIiIiKS1HXbO0glOzsbc+fOxbRp09CoUSMAOa0mVqxYgS1btuD48eP48ssvYTAYULVqVWzevBkLFy7EU089JXFyIiIiIiIiIiIiIunE7UrfnTt3QqPR4JZbbrl0W8+ePfHxxx9j7969qF27NgwGw6X7GjVqhD179kgRlYiIiIiIiIiIiChuxG3R98SJEyhXrhyWLVuGzp07o3Xr1pgwYQL8fj8uXryI5OTky45PTEzEuXPnJEpLREREREREREREFB/itr2Dy+XCqVOnMHv2bIwZMwYulwtjxoxBMBiEx+OBUqm87HiVSoVAIHDdceVyORITE2MVO98UCkVc5qLig9dY0adUKiEIAn+OFDN8nCj6/n5+FI8/R15fFEu8voiIiIguF7dFX4VCAafTiYkTJ6JChQoAgBEjRmDEiBHo0aMHnE7nZcf7/X5oNJrrjhsKhZCRkRGTzAWRmJgYl7mo+OA1VvQFAgEolUr+HClm+Djx//buPC6qev/j+HvYNwFBVAQl9w0FQkizUlMxd7G8aeWS163sal5bTDNvdU1/ZZm5paZp2bWFymxRUUtbzAVTxK3cUW8qixsisp3fH+YU1w0VPMPM6/l48MA558z3fL5zvg/O+ObLd8q+i78At8XryPhCaWJ8XZ/g4GCzSwAAAKXMZpd3qFixolxcXKyBryRVr15d58+fV1BQkNLS0oocn56erqCgoFtdJgAAAAAAAADYFJsNfSMjI5Wfn69ff/3Vum3v3r3y9vZWZGSkdu3apezsbOu+TZs2KTIy0oxSAQAAAAAAAMBm2Gzoe9ttt6l169Z67rnntG3bNiUlJWnSpEn629/+pmbNmqlKlSoaNWqUdu/erdmzZys5OVk9evQwu2wAAAAAAAAAMJXNhr6S9Oqrr6pu3brq27evhg4dqrZt22rkyJFydnbWjBkzlJmZqe7du+uLL77QtGnTFBoaanbJAAAAAAAAAGAqm/0gN0ny8fHRhAkTNGHChEv2hYWFaeHChSZUBQAAAAAAAAC2y6Zn+gIAAAAAAAAArg+hLwAAAAAAAADYEUJfAAAAAAAAALAjhL4AAAAAAAAAYEcIfQEAAAAAAADAjhD6AgAAAAAAAIAdcTG7AAAAAAC2xTAMFZw7p9zMzAtfJ04oNzNT+efOyc3fX24BAXIrX/7Cd39/Obm6ml0yAAAA/oLQFwAAALBjhmEo/+zZIuGt9fsf/z5/mX2F588X+xwuvr5yK19e7n8Ng6/2vXx5Obu7l2KvAQAAHBuhLwAAAFBGGIahvNOnlXeFoPZy389nZsrIy7t8gxZLkZm7XqGh8m/U6JKQ1j0gQG4BAXL29FTuyZNXPf/59HSd2b37wszgs2ev2BcXb+9rhsPuAQFy/UuY7OzpWUqvLAAAgH0h9AUAAECpyTt9WsdXr9bvp0/LrW5d+TduzAzPKzAMQ9mHDytz40ad3rXr8iHuyZMy8vMv+3yLs7N1Fq1bQIC8b7tN5W+//Yphqlv58nL185PF2fm66vQKDS32sQXnz1+ovRjhdNb+/co9cUL5p09fsT1nT8/L9qdyRIQqPvDAdfcFAADAXhH6AgAAoERlHz6so4mJ+j0xUek//1xklqmTm5v8GzdWQEyMApo0UWBMjNwrVDCxWvMU5uXp1LZtyti4UZlJScpMSlLO0aOSJIuLi9wCAqwzXcvVrn3NZRNcy5WTxcm2PqfZ2d1dnpUry7Ny5WI/pzAvT7knT/4ZCv91KYq/zF7OO3FCJ48cUW5mpvbPn6/au3er4fPPl2JvAAAAyg5CXwAAANwUo7BQJ1NSdHT5cv2emKjTO3ZIknxq1VKtgQNVOS5OoZGR2rtqlTI3blTmxo3aN3eu9sycKUnyrl5dATExCmzSRAExMSpXu7bNhZclITczU5mbNikzKUkZGzfq5JYtKsjJkSR5hoaqQrNmCvjjNfCtV09OLo75Vt3J1VUeQUHyCAoq9nN+fekl7ZwxQ77166vq/feXYnUAAABlg2O+kwQAAMBNKcjJUdpPP+loYqKOrlhxYYaqk5MCY2PV8IUXFNy2rXxq1rQe7xUYqCrt26tK+/bW55/cutUagB5btUqHPv5YkuTq56eA6GhrAFo+KkouXl6m9PNGGYahrL17L4Tcf/Qxa88eSRdm8fqFh+u23r0v9LFJE3kGB5tccdnWdNIkpaWkaPNTT8mnRg2Vj4oyuyQAAABTEfoCAACgWM5nZOjoypU6mpio42vWqCA7W85eXqrUqpUqx8WpcuvWcgsIKFZbzh4eCoyNVWBsrGrrQkh6dv/+P5c62LhRx779VtKFtWr9GjYssiSEZ5UqpdjT61dw7pxObN2qzA0brEs15J44IUly9fdXQJMmqtajhwKaNJF/RESZC7FtnZOrq2JnzdLqDh20vn9/tVi69LqWlAAAALA3hL4AAAC4LMMwlLVnj3V93sykJMkw5BEcrGo9eqhyXJwqNGsmZw+Pmz6XxWKRT40a8qlRQ2EPPihJyj150rocQubGjTr4n/9o39y5kiTPKlWKhMC+DRrc0uUQco4dU8YfdWUmJelkSop17WKfmjVVOS7uwpIVMTHyqVnTLpersDVuAQFqOn++vu/cWev799fdn34qZ09Ps8sCAAAwBaEvAAAArArz85WZlHRhfd4VK3R23z5Jkl94uOqOGKHguDj5NWoki8VS6rW4+furcuvWqty69YXa8vJ0ascO67rAGRs26MgXX0iSnL28VP72260hcPnbb5ebn1+J1GEUFOj0r79eOOcfQW92aqokycndXeUjIlRr0KALIXR0tNwDA0vkvLh+vvXqKXraNK3v31+bn3lG0W+9dUvGKgAAgK0h9AUAAHBweVlZOr569YX1eVetUt6JE7K4uiqoeXPVHDBAldu2lVdIiNllysnVVeUjIlQ+IkI1BwyQJGUfPmxdMzczKUm/vfWWVFgoWSzyrVvXui5wQJMm8r7ttmIFgHlZWTrxyy/WWbyZv/yi/DNnJEnuQUEKiIlRjUcfvbBUQ6NGcnJzK9V+4/oEt2un+s8+q50TJ8qvfn3Vfvxxs0sCAAC45Qh9AQAAHFD2kSM6umKFjiYmKn3tWhXm5sq1fPkLM2vj4lSxRQu5litndpnX5BUaKq/QUIV26yZJyj97Vic2b7aGwIeXLNGBhQsl/RHY/vHBaYExMfL7I7DNPnzYOns4MylJp3bu/DM4rl9fofHxCvwjOPaqVo2Zo2VAnX/8Q6d37ND28eNVrm5d62xxAAAAR0HoCwAA4AAMw9CplBT9npioo4mJOrVtmyTJu3p11Xj0UVVu104BTZrc0nVxS4OLt7eC7rpLQXfdJUkyCgt15rffinxA3O9Ll0q6sDSDq6+vzqelWZ9b/vbbVXf4cOsSEa6+vqb1BTfOYrEo6o03lLVvn5Ief1wtvvpK5WrXNrssAACAW6Zsv6sHAADAFRWcP6/0tWsvLNuQmKhzv/8uWSwKaNJEDceMUeW4OPnUqmXXM1ctTk7yrVdPvvXqqXrv3pKknLQ0awB8PiND5SMjFRATI9969cp86I0/uXh56Y5339Wa9u21rl8/tfj6a7n5+5tdFgAAwC3Bu1oAAAA74paXp9SEBB1NTNTx775T/tmzcvb0VMWWLVXvmWdUuXVruVeoYHaZpvIIClKV9u1VpX17s0tBKfMKCVHs3Ln68YEHtHHIEDVbuJBgHwAAOATe8QAAANiJGsePq9nevfolKUkelSopND5elePiFNS8uZw9Pc0uDzBFYEyMIidO1OaRI7X95ZfV6MUXzS4JAACg1BH6AgAA2IkT3t5KrlpVT86aJf/GjWVxcjK7JMAmhPXqpdO7dmnvnDnyrVdPYb16mV0SAABAqSL0BQAAsBMnvL11wttb5SMjzS4FsDkNx47V6V9/1ZZRo+RTs6YCY2PNLgkAAKDUMP0DAAAAgN1zcnFRzNtvyys0VBsGDFD24cNmlwQAAFBqCH0BAAAAOAQ3f381nT9fBefPa33//srPzja7JAAAgFJB6AsAAADAYZSrXVtNZszQqe3btfmf/5RhGGaXBAAAUOIIfQEAAAA4lMqtW6vhmDE6smSJfps61exyAAAAShwf5AYAAADA4dR67DGd2rlTOydOlG+dOgq+7z6zSwIAACgxzPQFAAAA4HAsFouiXntN5aOitOkf/9DpXbvMLgkAAKDEEPoCAAAAcEjOHh6KnTtXLj4+Wtevn3IzM80uCQAAoEQQ+gIAAABwWJ6VK+uOefOUc+yYNgwerMK8PLNLAgAAuGmEvgAAAAAcWvmoKEVNmqT0n35SyrhxZpcDAABw0/ggNwAAAAAOr+r99+v0zp3aPWOGfOvXV/Xevc0uCQAA4IYx0xcAAAAAJDV47jlVat1aW8eMUfq6dWaXAwAAcMMIfQEAAABAksXZWU2mT5f3bbdpw4ABOnvokNklAQAA3BBCXwAAAAD4g6uvr5rOny+joEDrH31U+WfPml0SAADAdSP0BQAAAIC/8KlRQzEzZ+r0rl3aNGyYjMJCs0sCAAC4LoS+AAAAAPA/KrZsqfBx4/T70qXa9cYbZpcDAABwXVzMLgAAAAAAbFHNAQN0escO/frGG/KtV08hnTqZXRIAAECxMNMXAAAAAC7DYrEoYuJEBURH65fhw3Vy2zazSwIAACgWQl8AAAAAuAJnd3fFzp0rN39/rX/0UZ1PTze7JAAAgGsi9AUAAACAq/CoWFF3vPuucjMytGHAABXm5ppdEgAAwFUR+gIAAADANfg3bqyoyZOVsWGDkkePlmEYZpcEAABwRXyQGwAAAAAUQ2jXrjq9a5d+mzJFfg0aqEb//maXBAAAcFnM9AUAAACAYqr/9NOq3K6dUsaNU9oPP5hdDgAAwGWVmdB3zJgx6t27t/XxkSNH1L9/f0VGRqp9+/Zas2aNidUBAAAAcAQWJydFT50qn1q1tGHwYJ09cMDskgAAAC5RJkLfn3/+WQkJCdbHhmHo8ccfl7+/vxISEhQfH69hw4bp0KFDJlYJAAAAwBG4+vio6fz5ksWidf36Ke/MGbNLAgAAKMLmQ9/s7GyNHTtWt99+u3XbunXrtH//fr300kuqVauWBg0apKioqCLBMAAAAACUFu+wMMXOnq2svXuVNHSojIICs0sCAACwsvnQd/LkyYqNjVVsbKx1W3Jysho0aCAfHx/rtujoaG3ZssWMEgEAAAA4oKDmzdXo5Zd1bOVK7Xz1VbPLAQAAsLLp0Hfz5s1atmyZnn322SLb09LSVLFixSLbAgMDdfTo0VtZHgAAAAAHV71vX93Wu7d+mzpVhz//3OxyAAAAJEkuZhdwJbm5uRozZoxGjx4tPz+/IvvOnTsnV1fXItvc3NyUl5d3zXadnZ0VGBhYorWWBBcXF5usC/aDMVb2ubq6ymKxcB1Ravg5UfZdfH9ki9eR8YXSZPb4ajljhpYdOKDNI0eqSlSUgqKjTasFAABAsuHQd/r06QoLC1P79u0v2efu7q6srKwi23Jzc+Xh4XHNdgsKCpSRkVFidZaUwMBAm6wL9oMxVvbl5eXJ1dWV64hSw8+Jsu/iL8Bt8ToyvlCabGF8Rc2YodXt2yvx/vvV8ptv5FGpkqn1XE1wcLDZJQAAgFJms8s7fPnll/rxxx8VFRWlqKgozZ07V0lJSYqKilKlSpWUlpZW5Pj09HQFBQWZVC0AAAAAR+YeGKim8+cr79Qprf/731WQk2N2SQAAwIHZbOj7/vvv66uvvtLixYu1ePFi9ejRQ+Hh4Vq8eLEiIiK0a9cuZWdnW4/ftGmTIiMjTawYAAAAgCPza9BA0W+9pRO//KIto0bJMAyzSwIAAA7KZkPfkJAQhYWFWb98fX3l4eGhsLAwxcbGqkqVKho1apR2796t2bNnKzk5WT169DC7bAAAAAAOrEqHDqo3cqQOffyx9s6ebXY5AADAQdls6Hs1zs7OmjFjhjIzM9W9e3d98cUXmjZtmkJDQ80uDQAAAICDqztihKp07KhtL7+s/L/8dSIAAMCtYrMf5Pa/RowYUeRxWFiYFi5caFI1AAAAAHB5FicnRU+dqpopKXLx8jK7HAAA4IDK5ExfAAAAALBlzh4eCoyJMbsMAADgoAh9AQAAAAAAAMCOEPoCAAAAAAAAgB0h9AUAAAAAAAAAO0LoCwAAAAAAAAB2hNAXAAAAAAAAAOwIoS8AAABK1cGDp7V8+V4ZhmF2KQAAAIBDIPQFAABAqfn++8Nq1+4zdenysQYOXKGMjHNmlwQAAADYPUJfAAAAlIoFC3bo4YeXqkoVbz3//F1auTJVrVolaPnyA2aXBgAAANg1Ql8AAACUqPz8Qj3//E967rkf1apVVS1Z0lVjx96tpUu7q2JFLz36aKJGjFitM2dyzS4VZdy5c/laseKgpk3bqPz8QrPLAQAAsBkuZhcAAAAA+3H6dK6GDFmp1asPa/DgRnr++Tvk7HxhnkH9+gH65ptueuONTZo2LVk//fRfTZ7cUs2bVzG5apQl6enntGLFQSUmHtSaNYeVk1MgSUpOrq8JE+6SxWIxuUIAAADzEfoCAACgRBw4cFp9+y7T/v2n9Npr9+jhh+tdcoybm7NGjYpV27ZhGj58tXr0+EoDB4Zr1KhYeXry1hSXMgxDe/ac1PLlF4LeTZuOyTCkkBAf9epVT3FxYUpKytTrr69TaGg5PfFEpNklAwAAmI531gAAALhp69b9rr//PVGS9OGHHXXnnVefvRsdXUmJid01fvwGzZmzTd99d1hvvdVSkZEVb0W5sHH5+YXauPGoEhMvBL3795+WJEVEBOmpp5ooLi5MDRoEWGf1duvWWHv3pumVVzYoJMRH8fG1zCwfAADAdIS+AAAAuCmLFu3SqFE/KizMVwsWtFP16n7Fep6Xl6vGj2+udu3CNGLEGnXu/IWGD4/S8OG3y9WVj55wNGfO5Gr16sNKTDygVasO6eTJ83J3d9Zdd1XRkCGN1aZNmIKDvS/7XCcniyZPbqljx7I1YsRqVa7spWbNWDYEAAA4LkJfAAAA3JCCgkKNH79Bb7+9VffcE6JZs9rIz8/9utu5555QffvtAxo7dq3eeOMXrVyZqrfeaqU6dcqXQtWwJYcPZ1nX51279r/KyytU+fLuiosLU1xcmFq0CJW3t2ux2nJ3d9bcuXHq2vUL9e+pRQkWAAAgAElEQVSfqCVLuqp2bcYQAABwTIS+AAAAuG5ZWbkaOvRbrViRqn79Guill+6Ui8uNz87183PXW2+10n333aZnnvlB7dp9plGjYjRwYCM5OTnOB3OdOZOrDz7YpY0bj6paNV/VquWnmjX9VbOmnypU8CzzH1JmGIZSUtKt6/Nu354hSapZ008DBoQrLu42NWlS0frhf9fL399dCxe2V6dOi/Xww0v11VfdVLGiV0l2AQAAoEwg9AUAAMB1OXz4jPr1W65ffz2h8eOb69FHG5ZY2x06VFdMTCU9/fQPevHFdUpMPKg332ypqlXLldg5bNHvv5/VO++kaOHCnTpzJk9hYeX03XeHlJNTYD3G19dNNWv+GQJf+O6v6tV95eFhu2/rc3LytXbtf5WYeFArVqTq99/PysnJopiYSho79g61bRumWrX8S+x8VauW0/vv36f4+C/Vu/cyffZZ52LPFgYAALAXtvvuEAAAADYnKemY+vdPVG5ugd5/v71atgwt8XMEBXnp3Xfj9PHHv2ns2LW6994EvfhiM/XqVbfMz3T9X7t2Zertt7fq88/3qKDAUKdO1fXYYxGKiAhSYaGhI0eytHfvSe3Zc0p7957U3r2n9NNP/1VCwm5rGxaLFBpa7jKBsJ+Cg71Nec0yMnK0alWqEhMPaPXqw8rOzpeXl4tataqquLgw3XtvNQUGepTa+Rs3DtKsWW3Ur99yPfbYKs2bF3dTM9EBAADKGkJfAAAAFMtnn+3WyJHfKzjYWwsWdCrV9VItFosefLCumjevohEj1uipp77XsmUHNGnSPWX+z/UNw9Datb9r5sxkffvtIXl6uqh37/oaNKiRqlXztR7n5GRR1arlVLVqObVsWbVIG9nZedq7988g+OL3DRt2KTs733qcl5eLatS4dHZwzZp+JT77dc+ek0pMvLBsQ1LSMRUWGgoO9laPHnUUFxemZs2Cb+mM5DZtqmnChOZ69tkf9fzzP2nChLvs7pcGAAAAV0LoCwAAgKsqLDT02mtJmjJls5o1C9acOW0VEFB6szT/KjS0nD76qKPmzdumV17ZoFatPtH//d/d6tSpxi05f0nKzy/UN9/s18yZW5WcnKYKFTz1zDNN1KdPg+t+Pb28XNWoUQU1alShyHbDMHT0aPYlYfDmzce1ZMleGcafxwYHe192dnBIiE+x1tQtKChUUtIx6/q8+/adkiSFhwfqySejFBd3mxo1CjQ1aO3du4EOHcrStGlbFBpaTk88EWlaLQAAALcSoS8AAACuKDs7T8OHr9bXX+9Xr151NWHCXXJzc76lNTg5WTRgQCO1aFFVw4Z9p0GDVio+vpbGj28uf3/3W1rLjcjOztOHH/6q2bNTlJp6RjVq+OnVV+/W/ffXlqdnyb4dt1gsCg72VnCwt+66K6TIvpycfB04cFp79pwsEgp//vkenT6daz3O3d1Z1av7XnZ2sIuLk9asOazlyw9o5cpUnThxXq6uTrrzzioaMCBcbdqEKTTUp0T7dLNGjYrR4cNn9MorGxQS4qP4+FpmlwQAAFDqCH0BAABwWb//flb9+i3Xtm3pGjeuqQYNamTqrM3atf21ZElXTZ26WW+++YvWrftdr79+zyVLH9iK9PRzmjdvuxYs2K4TJ86rSZNKGjeumdq1C5OT061/HT08XFSvXoDq1Qsost0wDKWnn7OGwBfXD965M1PLlh1QQcGf04OdnS0qKDDk7++u1q2rKi7uNrVsGapy5dxudXeKzcnJosmTW+rYsWyNGLFalSt7qVmzKmaXBQAAUKoIfQEAAHCJrVvT1K/fcp05k6f589upbdsws0uSJLm6Oumf/4xWmzbVNGzYaj300FL17dtAY8feIS+vkl2j9kbt23dKs2Zt1Sef/Kbz5wsUFxemxx6LUGxsZbNLuyyLxaKgIC8FBXmpadPgIvtycwt08OCZP2YGn1RWVp7uuSdEMTGVy9QHo7m7O2vu3Dh17fqF+vdP1JIlXUt1TWoAAACzEfoCAACgiK++2qdhw75ThQqeWrKkq+rXD7j2k26xxo2DtGxZvP7v/5I0e/ZWrVlzWFOmtFRMjHnBalLSMc2cmaxlyw7Izc1ZDzxQW4MHN1atWv6m1XSz3NycVbu2v2rXLrt9uMjf310LF7ZXp06L9fDDS/XVV93K/IcCAgAAXEnZ+fU8AAAASpVhGHrzzV80aNBKNWxYQd98E2+Tge9FHh4uGjeuqRISOqmgwFB8/JcaP369zp8vuGU1FBYaWrbsgLp2/UJdunyhn3/+XcOGRWnDhl567bV7ynTga4+qVi2n99+/TxkZOerde5nOns0zuyQAAIBSQegLAAAA5eTk64knvtOrrybp/vtr6ZNPOqpCBU+zyyqWZs2qaNWq+9WzZ11Nn56sDh0+1/btGaV6zpycfH3wwU61aPGx+vdP1O+/n9VLL92pjRsf0rPPxigoiBmktqpx4yDNmtVG27dn6LHHVik/v9DskgAAAEocoS8AAICDO348Ww888JU+/3yPRo2K0VtvtZKHR9laBczHx02TJt2j9967T+np59Shw+eaOnVziQd6J07kaMqUX3THHYv09NM/yMvLVTNm3Ku1a3tqwIBweXvbxrrCuLo2bappwoTmWrkyVWPHrpVhGNd+EgAAQBlStt7NAwAAoETt2JGhPn2W6cSJ83rnnbbq0KG62SXdlDZtqunbb3voued+0IQJG5WYmKopU1qqRg2/m2r30KEzmj07RYsW7VJ2dr5ataqqxx5rrObNq8hisZRQ9biVevduoNTUM5o+PVmhoT4aOjTS7JIAAABKDKEvAACAg1q+/ICGDv1Wfn7u+vzzLmrcuILZJZWIwEAPzZrVRp9/vldjxvyotm0/1dixd6hv3wbXHdCmpKRr5sxkffnlPlksUnx8LQ0ZEmHTax2j+J57LlaHD2dp/PgNCgnxUbdutcwuCQAAoEQQ+gIAADgYwzA0c+ZWjR+/Xo0bB+ndd+NUubK32WWVKIvFou7da6lZs2CNHLlGo0f/pGXLDuiNN1qoShWfqz7XMAytWXNYM2Zs1Y8/HpGPj6sGDmykAQPCr/lclC1OTha9+WZLHTuWrSefXK3Klb3VtGmw2WUBAADcNNb0BQAAcCC5uQX65z/X6N//Xq9OnWro0087213g+1fBwd764IP2mjjxLm3ceEz33pugzz7bfdk1XPPyCpWQ8Jvatv1UDz20VHv2nNDzz9+hpKSH9cILTQl87ZS7u7Pmzm2ratV89eijy7V79wmzSwIAALhphL4AAAAOIiMjRw8++LU++ug3jRhxu2bObC0vL/v/wy+LxaI+fRpo5cr7VadOeT3xxHcaNGilMjLOSZLOnMnV229vVdOmizRs2GoVFBiaPLmF1q3rpccfj5Cvr5vJPUBpK1/eQwsX3ic3N2c98sgyHT+ebXZJAAAAN8X+3+UDAABAv/12Qn37LtPRo9maPv1excc73tql1av76fPPO2vmzK2aNClJ69cfVceO1fXZZ7t15kye7rwzWK++erfuvbcqH87mgKpV89V7792n7t2/VJ8+y/TZZ53l5eVqdlkAAAA3hJm+AAAAdu677w6pc+fFys7O16efdnbIwPciZ2cnPfFEpL75Jl4VK3rq/fd3qmXLqlq6NF4JCZ3VunU1Al8HFhERpLffbq1t2zI0ZMgq5ecXml0SAADADWGmLwAAgJ0yDEPz5m3XuHE/q169AM2f306hoaxLK0kNGgRq+fLuys7OV7lyLN+AP7VtG6ZXXmmuUaN+1Nixa/XKK835RQAAAChzCH0BAADsUF5eoV54Ya0WLNihdu3CNG3avfL25k/V/8rZ2YnAF5fVp08DHTp0RtOnJys01EdDh0aaXRIAAMB1IfQFAACwMydPntegQSv1449H9PjjERo9OlZOTsxUBK7Hc8/F6vDhLI0fv0EhIT7q1s1xl0UBAABlD6EvAACAHTl3zkudOi3WoUNnNHlyCz34YF2zSwLKJCcni958s6WOHcvWk0+uVuXK3mraNNjssgAAAIqFD3IDAACwE1lZvkpJaaoTJ3L08ccdCXyBm+Tu7qy5c9uqWjVfPfrocu3efcLskgAAAIqF0BcAAMBOnD3rKy+vLH3zTbzuuIMZiUBJKF/eQwsX3ic3N2c98sgyHT+ebXZJAAAA10ToCwAAYCcqVTqs8PANCgvzNbsUwK5Uq+ar9967T+np59SnzzJlZ+eZXRIAAMBVEfoCAAAAwDVERATp7bdba9u2DA0Zskr5+YVmlwQAAHBFhL4AAAAAUAxt24bplVeaa+XKVI0du1aGYZhdEgAAwGW5mF0AAAAAAJQVffo00KFDZzR9erJCQ300dGik2SUBAABcgtAXAAAAAK7Dc8/F6vDhLI0fv0EhIT7q1q2W2SUBAAAUQegLAAAAANfBycmiN99sqWPHsvXkk6tVubK3mjYNNrssAAAAK5te0zc1NVVDhgxRTEyM7rnnHk2cOFHnz5+XJB05ckT9+/dXZGSk2rdvrzVr1phcLQAAAABH4e7urLlz26paNV89+uhy7d59wuySAAAArGw29M3NzdWQIUPk5uamDz/8UJMmTdLKlSs1efJkGYahxx9/XP7+/kpISFB8fLyGDRumQ4cOmV02AAAAAAdRvryHFi68T25uznrkkWU6fjzb7JIAAAAk2XDou3XrVqWmpmrChAmqWbOmYmNjNXz4cH355Zdat26d9u/fr5deekm1atXSoEGDFBUVpYSEBLPLBgAAAOBAqlXz1Xvv3af09HPq02eZsrPzzC4JAADAdkPfGjVqaPbs2fL29rZus1gsys3NVXJysho0aCAfHx/rvujoaG3ZssWMUgEAAAA4sIiIIM2c2VrbtmVoyJBVys8vNLskAADg4Gw29A0ICNCdd95pfVxYWKiFCxcqOjpaaWlpqlixYpHjAwMDdfTo0VtdJgAAAAAoLi5M//73nVq5MlVjx66VYRhmlwQAAByYi9kFFNeECRO0c+dOJSQk6N1335Wrq2uR/W5ubsrLu/afUjk7OyswMLC0yrxhLi4uNlkX7AdjrOxzdXWVxWLhOqLU8HOi7Lv4/sgWryPjC6XJVsbXyJH3KCOjQK+/vk516lTSU081NbskAADgoGw+9DUMQ+PHj9eiRYs0ZcoU1a5dW+7u7srKyipyXG5urjw8PK7ZXkFBgTIyMkqr3BsWGBhok3XBfjDGyr68vDy5urpyHVFq+DlR9l38BbgtXkfGF0qTLY2vESMaaffu4xoz5juVL29Rt261zC7pEsHBwWaXAAAASpnNLu8gXVjSYfTo0frwww81efJktWnTRpJUqVIlpaWlFTk2PT1dQUFBZpQJAAAAAJIkJyeL3nyzpe64o7KefHK1srPzzS4JAAA4IJsOfSdOnKgvv/xSU6dOVVxcnHV7RESEdu3apezsbOu2TZs2KTIy0owyAQAAAMDKw8NFCxbcp5kz28jT09nscgAAgAOy2dB3y5YtWrBggYYNG6bw8HClpaVZv2JjY1WlShWNGjVKu3fv1uzZs5WcnKwePXqYXTYAAAAAyNfXTe3b3yaLxWJ2KQAAwAHZ7Jq+y5cvlyS9/vrrev3114vs2759u2bMmKExY8aoe/fuqlatmqZNm6bQ0FAzSgUAAAAAAAAAm2Gzoe+zzz6rZ5999or7w8LCtHDhwltYEQAAAAAAAADYPptd3gEAAAAAAAAAcP0IfQEAAAAAAADAjhD6AgAAAAAAAIAdIfQFAAAAAAAAADtC6AsAAAAAAAAAdoTQFwAAAAAAAADsCKEvAAAAAAAAANgRQl8AAAAAAAAAsCOEvgAAAAAAAABgRwh9AQAAAAAAAMCOEPoCAAAAAAAAgB0h9AUAAAAAAAAAO0LoCwAAAAAAAAB2hNAXAAAAAAAAAOwIoS8AAAAAAAAA2BFCXwAAAAAAAACwI4S+AAAAAAAAAGBHCH0BAAAAAAAAwI4Q+gIAAAAAAACAHSH0BQAAAAAAAAA7QugLAAAAAAAAAHaE0BcAAAAAAAAA7AihLwAAAAAAAADYEUJfAAAAAAAAALAjhL4AAAAAAAAAYEcIfQEAAAAAAADAjhD6AgAAAAAAAIAdIfQFAAAAAAAAADtC6AsAAAAAAAAAdoTQFwAAAAAAAADsCKEvAAAAAAAAANgRQl8AAAAAAAAAsCOEvgAAAAAAAABgRwh9AQAAAAAAAMCOEPoCAAAAAAAAgB0h9AUAAAAAAAAAO0LoCwAAAAAAAAB2hNAXAAAAAAAAAOwIoS8AAAAAAAAA2BFCXwAAAAAAAACwI4S+AAAAAAAAAGBHCH0BAAAAAAAAwI4Q+gIAAAAAAACAHSH0BQAAAAAAAAA7QugLAAAAAAAAAHaE0BcAAAAAAAAA7EiZDn1zc3M1duxYxcTEqHnz5pozZ47ZJQEAAAAAAACAqVzMLuBmvPrqq9q8ebPeffddHT16VM8884yqVKmijh07ml0aAAAAAAAAAJiizM70zc7O1scff6zRo0crPDxcbdq00YABA7Rw4UKzSwMAAAAAAAAA05TZ0HfXrl3Kzc1VdHS0dVt0dLRSUlKUn59vYmUAAAAAAAAAYJ4yG/qmpaXJz89P7u7u1m0VKlRQXl6eMjMzTawMAAAAAAAAAMxTZtf0PXfunNzc3Ipsu/g4Nzf3is9zc3NTcHBwqdZ2o2y1LtgPxljZtnbtWrNLgAPg50TZZus/JxhfKE2MLwAAgD+V2Zm+7u7ul4S7Fx97enqaURIAAAAAAAAAmK7Mhr6VKlXS6dOniwS/aWlpcnNzk5+fn4mVAQAAAAAAAIB5ymzoW79+fbm6umrz5s3WbZs2bVLDhg3l4lJmV60AAAAAAAAAgJtSZkNfT09PdevWTS+++KK2bt2qVatWad68eerTp4/ZpQEAAAAAAACAaSyGYRhmF3Gjzp07p3/9619KTEyUt7e3+vfvr/79+5tdFgAAAAAAAACYpkyHvgAAAAAAAACAosrs8g4lJTU1VUOGDFFMTIzuueceTZw4UefPn5ckHTlyRP3791dkZKTat2+vNWvWXLaNL774Qr169SqyzTAMTZs2Tffcc49iYmI0bNgwpaenX7WWq53v3nvvVd26dS/5eu65526ob5I0duzYS9qbP3/+tV4yXAdHHl87d+7UQw89pKioKHXr1k0//PDDNV8vW1VWrqMkrVixQh06dFBUVJR69uyplJSUq7aXm5ursWPHKiYmRs2bN9ecOXMue9zBgwfVuHFj5efnX7U93BhHHmPci0qfI48ve7oX2Sp7Hl8X5ebmqlOnTlq7dm2R7d999506d+6sxo0bq2vXrlfsHwAAgCkMB3b+/Hmjffv2xj/+8Q9jz549xvr1643WrVsbEyZMMAoLC40uXboYI0aMMHbv3m3MmjXLaNy4sZGamlqkjZ9//tmIiIgwevbsWWT7okWLjLvuustYt26d8euvvxq9evUyBg4ceMVarnW+jIwM4/jx49avxYsXGw0bNjRSUlKuu28X9ezZ03jnnXeKtJudnX2jLyf+hyOPr4yMDKNJkybGM888Y+zZs8dISEgwIiIijK1bt97MS2qKsnQdf/vtN6NRo0ZGQkKCcfDgQePFF180mjVrZmRlZV2xzZdfftno2LGjkZKSYqxYscKIiooyvvrqqyLH/Pe//zXatWtn1KlTx8jLy7velxDX4OhjjHtR6XLk8WVP9yJbZe/jyzAMIycnxxg6dKhRp04d46effrJu3717txEeHm68//77RmpqqvHOO+8YDRs2vKR/AAAAZnHo0Hfjxo1Gw4YNi7zZW7JkiXHnnXcaa9euNRo1amScOXPGuq9v377GG2+8YX08depUIzw83OjUqdMlb1SHDBli/Pvf/7Y+XrVqldGoUaMr1lKc812UnZ1ttGjRwpg+ffoN9e2i2NhYY926dVdsAzfHkcfX3LlzjRYtWhi5ubnW/WPHjjVGjBhxxTZtVVm6ju+++67RpUsX674zZ84YderUMbZs2XLZ9s6ePWs0atSoyH9ip0+fXqTOFStWGE2bNjU6d+5M6FtKHH2McS8qXY48vuzpXmSr7Hl8GcaFYLdLly7We+Bfx9r3339vTJw4scjxMTExxpIlS67YHgAAwK3k0Ms71KhRQ7Nnz5a3t7d1m8ViUW5urpKTk9WgQQP5+PhY90VHR2vLli3Wxxs2bNC8efMUFxd3Sdv+/v76/vvvdfToUeXk5Ojrr79Ww4YNr1hLcc530XvvvSeLxaK///3vN9Q3SUpLS9PJkydVvXr1K7aBm+PI4+vQoUMKDw+Xq6urdX+9evUuez5bV5auo7+/v/bt26ekpCQVFhbqs88+k4+Pj8LCwi7b3q5du5Sbm6vo6Ogi7aWkpFiXcfjhhx80YsQIjRkz5lovFW6QI48x7kWlz5HHlz3di2yVPY8vSUpKSlLz5s310UcfXbLv7rvv1rPPPitJysvL0yeffKLc3FxFRkZesT0AAIBbycXsAswUEBCgO++80/q4sLBQCxcuVHR0tNLS0lSxYsUixwcGBuro0aPWx++9954kad26dZe0PXToUD322GNq0aKFnJ2dVaFCBX344YdXrKU455MurCk2b948DR8+XO7u7jfUN0nas2ePXFxcNGXKFH3//fcqX768+vXrp+7du1+xTVwfRx5fgYGBl6yT99///lcnTpy4Ypu2qixdxw4dOujbb7/Vww8/LGdnZzk5OWnGjBny9/e/Ynt+fn5FrnWFChWUl5enzMxMVaxYUS+++KIkaf369VesCzfHkcfY3r17uReVMkceX/Z0L7JV9jy+JKlnz55X6f0Fe/fuVefOnVVQUKCRI0eqatWq13wOAADAreDQM33/14QJE7Rz506NHDlS586dKzIzRJLc3NyUl5dXrLaOHj0qd3d3TZ8+XYsWLVLt2rU1fPhw60zI/1Xc8y1dulSSFB8fX9xuSSraN0nat2+fpAszXubMmaMHHnhAL7zwgrV9lDxHGl/33XefduzYoYULFyovL09btmzRp59+Wuz+2TJbvo6nTp1Senq6Ro8erU8++US9e/fW008/rdTU1Cu25+bmdkl7kq5YA0qfI40x7kW3niONL3u+F9kqexpfxRUUFKSEhASNHTtWb731lpYvX35T7QEAAJQUh57pe5FhGBo/frwWLVqkKVOmqHbt2nJ3d1dWVlaR43Jzc+Xh4VGs9p555hk9+eSTatOmjSTpzTffVIsWLfTtt9/qwIEDmjVrlvX4OXPmFPt8S5cuVbt27eTp6WndlpSUpIEDB1ofDx48WEOGDLli3yTpoYceUseOHa2zG+rVq6eDBw9q0aJFat++/TX7iOJzxPFVq1YtTZgwQS+//LLGjx+vatWqqU+fPpo/f34xXjHbVBau46RJk1SjRg317dtXktSwYUOlpKRo/vz56tChwyXXMSws7JL/PF98/NcxgFvDEccY96JbxxHHV2hoqN3di2yVPY6vi+91rsXX11cNGjRQgwYN9Ntvv2nhwoVq165dsZ4LAABQmhw+9C0sLNSYMWP05ZdfavLkydY3lpUqVdKuXbuKHJuenq6goKBrtpmZmakjR46oTp061m3lypVTWFiYDh06pJ49exb5z2ylSpWUlJR0zfPl5uZq3bp1mjp1apHjwsPDtXjxYutjPz+/q/ZNurDe2v/+OVuNGjX0448/XrN/KD5HHV+S1LVrV3Xu3Nl6nv/85z8KCQm5Zv9sUVm5jikpKXrwwQeL7G/YsKH27dt32eu4b98+nT59Wrm5udbZcWlpaXJzc7NeZ9wajjrGuBfdGo46viT7uhfZKnsdX9eya9cuZWdn6/bbb7duq1Wrln755ZdrPhcAAOBWcPjlHSZOnKgvv/xSU6dOLfIhEhEREdY3cxdt2rSpWB/O4O/vLzc3tyJvPHNycnTkyBFVq1ZN/v7+CgsLs355eHgU63y//vqrzp8/X+QDSyTJw8OjSHsX/wN9pb5d3Dd48OAi23bu3KkaNWpcs38oPkcdX+vXr9ewYcPk5OSkihUrymKx6Ntvv9Udd9xRzFfOtpSV61ixYkX9+uuvRc6zd+9eVatW7bLXsX79+nJ1ddXmzZuLtNewYUO5uDj87wRvKUcdY9yLbg1HHV/2di+yVfY6vq5l6dKl+te//lVk2/bt2/n5BQAAbIZDh75btmzRggULNGzYMIWHhystLc36FRsbqypVqmjUqFHavXu3Zs+ereTkZPXo0eOa7To7O6tHjx6aMmWKfv75Z+3du1ejR49W+fLl1apVq8s+pzjn2717t0JCQuTl5XVTfZOke++9V99//73ee+89paam6oMPPtDixYv197//vZivHq7FkcdX9erV9cMPP2jBggU6dOiQpkyZouTkZOufVJYlZek69uzZU0uWLNGnn36q1NRUzZ49W+vWrdNDDz102fY8PT3VrVs3vfjii9q6datWrVqlefPmqU+fPjf+guG6OfIY415U+hx5fNnTvchW2fP4upYHHnhAqampmjx5sg4cOKD33ntPX3/99SW/yAIAADCN4cAmTpxo1KlT57JfeXl5xoEDB4yHH37YCA8PNzp06GD88MMPl23nrbfeMnr27FlkW05OjvHaa68ZLVu2NJo0aWIMHjzYOHLkyFXrudb5Zs2aZXTv3r1E+mYYhvH1118bHTt2NMLDw4327dsby5cvL1bbKB5HH19r1qwxOnToYERERBg9e/Y0tm7dWqy2bU1Zu46fffaZ0aFDByMyMtK4//77jQ0bNly1vezsbOOZZ54xIiMjjebNmxtz58697HHr1q0rcn1Rchx9jHEvKl2OPr7s5V5kq+x9fP1VnTp1jJ9++qnIto0bNxrdu3c3GjVqZHTo0MFYtWpVsdsDAAAobRbDMAyzg2cAAAAAAAAAQMlw6OUdAAAAAAAAAMDeEPoCAAAAAAAAgB0h9AUAAAAAAAAAO0LoCwAAAAAAAAB2hNAXADM4/J0AAAWsSURBVAAAAAAAAOwIoS8AAAAAAAAA2BEXswsAAMDWTZ06VdOmTSuyzWKxyMPDQxUrVtQdd9yhfv36qWbNmjd8joKCAi1atEjdu3eXl5fXzZYMAAAAAHBghL4AABRT69atVb9+fUlSYWGhsrKytGvXLn388cdasmSJpkyZopYtW95Q2yNHjtTSpUvVpUuXEqwYAAAAAOCICH0BACimNm3aqHv37pdsX7NmjYYOHaoRI0Zo8eLFCgsLu+62MzIySqJEAAAAAABY0xcAgJvVokULDR8+XNnZ2ZoxY4bZ5QAAAAAAHByhLwAAJeCRRx6Ru7u7EhMTlZ+fL0nKy8vTggUL9Le//U3R0dEKDw9Xq1at9MILLygzM9P63Lp162rDhg2SpJiYGPXu3du6Lzc3V7NmzVKHDh3UqFEjNWvWTCNHjtShQ4dubQcBAAAAAGUGoS8AACXA09NTDRo0UHZ2tnbu3Cnpwjq9r7zyilxcXPS3v/1NDz74oNzc3PTRRx9p4MCB1uc+8cQTCgkJkSQNHDhQ8fHxki6ExgMHDtQbb7whb29vPfLII7r77ruVmJioBx54QL/99tut7ygAAAAAwOaxpi8AACWkUqVKkqS0tDRt2bJFy5cvV+fOnTVp0iTrMfn5+YqPj9e2bdu0f/9+Va9eXf/4xz+0YcMGHTlyRIMGDZKvr68kacGCBVq3bp0GDBigp556ShaLRZLUu3dv9erVS6NHj1ZCQsKt7ygAAAAAwKYx0xcAgBLi5uYmScrKylLlypU1ceJEDR8+vMgxLi4uio6OlnTtD29LSEhQuXLl9OSTT1oDX0lq1KiR7rvvPqWkpGj37t0l3AsAAAAAQFnHTF8AAErI2bNnJUleXl6qXLmy4uPjlZ+fr+3bt2v//v1KTU3Vzp07tXbtWklSYWHhVdvav3+/goKC9Pbbb1+yPz09XZK0c+dO1a5duxR6AwAAAAAoqwh9AQAoIUeOHJEkVa1aVZL04Ycfavr06Tp+/LgkydfXVxEREapZs6aSk5NlGMYV28rKypJ0YamIadOmXfG4U6dOlVT5AAAAAAA7QegLAEAJOHXqlPbs2SNfX1/VqlVLS5cu1bhx41S3bl2NGzdODRs2VHBwsCRp3LhxSk5Ovmp7Xl5ekqQmTZrogw8+KPX6AQAAAAD2g9AXAIAS8NFHHyk/P1/t27eXs7OzvvrqK0nS66+/fsnyC/v27ZOkq870LVeunKpUqaI9e/YoJydHHh4eRfYvXrxYhw4dUnx8vEJDQ0u4NwAAAACAsowPcgMA4Cb9/PPPmj59ury8vDR48GBJkru7u6Q/1969aPHixdqwYYMkKT8/37rd1dVVkpSXl2fdFh8fr5MnT2rSpElF1v/ds2ePXnrpJb377rvy9/cvnU4BAAAAAMosZvoCAFBMK1eutK7baxiGzpw5ox07digpKUkeHh6aPHmyQkJCJEldunTR119/rSeeeEIdO3aUj4+PUlJStGHDBgUGBiojI0MnT560tl2pUiVJ0ujRo9W8eXP16dNHgwYN0o8//qj3339fmzZtUmxsrE6fPq1ly5bp3Llzeu211+Tj43PrXwgAAAAAgE2zGFf721IAAKCpU6de9sPUPD09FRISoqZNm6pv376qVq1akf3ffPON5syZowMHDsjDw0NVq1ZV165dFRUVpfj4eHXp0kWvvfaaJOngwYN6+umntWPHDoWEhGj58uWSpJycHL3zzjv65ptvdOjQIZUrV07169fX4MGDFRsbW/qdBwAAAACUOYS+AAAAAAAAAGBHWNMXAAAAAAAAAOwIoS8AAAAAAAAA2BFCXwAAAAAAAACwI4S+AAAAAAAAAGBHCH0BAAAAAAAAwI4Q+gIAAAAAAACAHSH0BQAAAAAAAAA7QugLAAAAAAAAAHaE0BcAAAAAAAAA7AihLwAAAAAAAADYkf8HFAwkwRolynwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_and_transactions_stats_fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are qualitatively similar to those obtained using the Precision top-$k$. In the test period, the number of compromised cards varied between 69 and 80. Once transactions from known compromised cards were removed, the remaining compromised cards varied between 38 and 55. Out of these remaining compromised cards, the fraud detector correctly detected between 19 and 35 cards, with an average number of 29 compromised cards detected per day (average Card Precision Top-$k$=0.33), that is, about 60% of the actual compromised cards were detected.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, it should be noted that once a compromised card has been detected, it is blocked and therefore should be removed from the pool of transactions. Let us amend the `card_precision_top_k` to remove compromised cards that have been detected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def card_precision_top_k(predictions_df, top_k, remove_detected_compromised_cards=True):\n",
    "\n",
    "    # Sort days by increasing order\n",
    "    list_days=list(predictions_df['TX_TIME_DAYS'].unique())\n",
    "    list_days.sort()\n",
    "    \n",
    "    # At first, the list of detected compromised cards is empty\n",
    "    list_detected_compromised_cards = []\n",
    "    \n",
    "    card_precision_top_k_per_day_list = []\n",
    "    nb_compromised_cards_per_day = []\n",
    "    \n",
    "    # For each day, compute precision top k\n",
    "    for day in list_days:\n",
    "        \n",
    "        df_day = predictions_df[predictions_df['TX_TIME_DAYS']==day]\n",
    "        df_day = df_day[['predictions', 'CUSTOMER_ID', 'TX_FRAUD']]\n",
    "        \n",
    "        # Let us remove detected compromised cards from the set of daily transactions\n",
    "        df_day = df_day[df_day.CUSTOMER_ID.isin(list_detected_compromised_cards)==False]\n",
    "        \n",
    "        nb_compromised_cards_per_day.append(len(df_day[df_day.TX_FRAUD==1].CUSTOMER_ID.unique()))\n",
    "        \n",
    "        detected_compromised_cards, card_precision_top_k = card_precision_top_k_day(df_day,top_k)\n",
    "        \n",
    "        card_precision_top_k_per_day_list.append(card_precision_top_k)\n",
    "        \n",
    "        # Let us update the list of detected compromised cards\n",
    "        if remove_detected_compromised_cards:\n",
    "            list_detected_compromised_cards.extend(detected_compromised_cards)\n",
    "        \n",
    "    # Compute the mean\n",
    "    mean_card_precision_top_k = np.array(card_precision_top_k_per_day_list).mean()\n",
    "    \n",
    "    # Returns precision top k per day as a list, and resulting mean\n",
    "    return nb_compromised_cards_per_day,card_precision_top_k_per_day_list,mean_card_precision_top_k\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of remaining compromised cards: [50, 50, 49, 47, 46, 44, 31]\n",
      "Precision top-k: [0.24, 0.32, 0.27, 0.25, 0.23, 0.26, 0.12]\n",
      "Average Precision top-k: 0.2414285714285714\n"
     ]
    }
   ],
   "source": [
    "nb_compromised_cards_per_day_remaining\\\n",
    ",card_precision_top_k_per_day_list\\\n",
    ",mean_card_precision_top_k=card_precision_top_k(predictions_df=predictions_df, top_k=100)\n",
    "\n",
    "print(\"Number of remaining compromised cards: \"+str(nb_compromised_cards_per_day_remaining))\n",
    "print(\"Precision top-k: \"+str(card_precision_top_k_per_day_list))\n",
    "print(\"Average Precision top-k: \"+str(mean_card_precision_top_k))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that the number of remaining compromised cards is lower, since detected compromised cards are removed from the pool of transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the number of transactions per day, \n",
    "#fraudulent transactions per day and fraudulent cards per day\n",
    "tx_stats=get_tx_stats(transactions_df, start_date_df=\"2018-04-01\")\n",
    "\n",
    "# Add the remaining number of compromised cards for the last 7 days (test period)\n",
    "tx_stats.loc[14:20,'nb_compromised_cards_per_day_remaining']=list(nb_compromised_cards_per_day_remaining)\n",
    "\n",
    "# Add the card precision top k for the last 7 days (test period) \n",
    "tx_stats.loc[14:20,'card_precision_top_k_per_day']=card_precision_top_k_per_day_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "# Plot the number of transactions per day, fraudulent transactions per day and fraudulent cards per day\n",
    "\n",
    "cmap = plt.get_cmap('jet')\n",
    "colors={'card_precision_top_k_per_day':cmap(0), \n",
    "        'nb_compromised_cards_per_day':cmap(200),\n",
    "        'nb_compromised_cards_per_day_remaining':cmap(250),\n",
    "       }\n",
    "\n",
    "fraud_and_transactions_stats_fig, ax = plt.subplots(1, 1, figsize=(15,8))\n",
    "\n",
    "# Training period\n",
    "start_date_training = datetime.datetime.strptime(\"2018-07-25\", \"%Y-%m-%d\")\n",
    "delta_train = delta_delay = delta_test = 7\n",
    "\n",
    "end_date_training = start_date_training+datetime.timedelta(days=delta_train-1)\n",
    "\n",
    "# Test period\n",
    "start_date_test = start_date_training+datetime.timedelta(days=delta_train+delta_delay)\n",
    "end_date_test = start_date_training+datetime.timedelta(days=delta_train+delta_delay+delta_test-1)\n",
    "\n",
    "get_template_tx_stats(ax, fs=20,\n",
    "                      start_date_training=start_date_training,\n",
    "                      title='Number of fraudulent transactions per day \\n and number of detected fraudulent transactions',\n",
    "                      delta_train=delta_train,\n",
    "                      delta_delay=delta_delay,\n",
    "                      delta_test=delta_test,\n",
    "                      ylim=150\n",
    "                     )\n",
    "\n",
    "ax.plot(tx_stats['tx_date'], tx_stats['nb_compromised_cards_per_day'], 'b', color=colors['nb_compromised_cards_per_day'], label = '# compromised cards per day - Original')\n",
    "ax.plot(tx_stats['tx_date'], tx_stats['nb_compromised_cards_per_day_remaining'], 'b', color=colors['nb_compromised_cards_per_day_remaining'], label = '# compromised cards per day - Remaining')\n",
    "ax.plot(tx_stats['tx_date'], tx_stats['card_precision_top_k_per_day']*100, 'b', color=colors['card_precision_top_k_per_day'], label = '# detected compromised compromised cards per day')\n",
    "\n",
    "ax.legend(loc = 'upper left',bbox_to_anchor=(1.05, 1),fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_and_transactions_stats_fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This last implementation of `card_precision_top_k` is the one included in the [shared functions](shared_functions) notebook and used in the next chapters. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
